Systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking

ABSTRACT

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. Application No.17/820,439, filed Aug. 17, 2022, which is a continuation-in-part of U.S.Pat. Application No. 17/662,734, filed May 10, 2022. U.S. ApplicationNo. 17/820,439 also claims the benefit of U.S. Provisional Pat.Application Nos. 63/235,010, filed Aug. 19, 2021, 63/241,427, filed Sep.7, 2021, 63/276,268, filed Nov. 5, 2021, 63/264,805, filed Dec. 2, 2021,63/264,913, filed Dec. 3, 2021, and 63/296,116, filed Jan. 3, 2022. U.S.Pat. Application No. 17/662,734 is a continuation of U.S. Pat.Application No. 17/367,549, filed Jul. 5, 2021, which is a continuationof U.S. Pat. Application No. 17/350,836, filed Jun. 17, 2021, which is acontinuation-in-part of U.S. Pat. Application No. 17/213,966, filed Mar.26, 2021, which is a continuation of U.S. Pat. Application No.17/142,120, filed Jan. 5, 2021, which claims the benefit of U.S.Provisional Pat. Application No. 62/958,032, filed Jan. 7, 2020. U.S.Pat. Application No. 17/350,836 claims the benefit of U.S. ProvisionalPat. Application Nos. 63/201,142, filed Apr. 14, 2021, 63/041,252, filedJun. 19, 2020, 63/077,044, filed Sep. 11, 2020, 63/077,058, filed Sep.11, 2020, 63/089,790, filed Oct. 9, 2020, and 63/142,873, filed Jan. 28,2021. Each one of the above-listed disclosures is incorporated herein byreference in its entirety. Also, U.S. Pat. No. 10,813,612 isincorporated herein by reference in its entirety. Any and allapplications for which a foreign or domestic priority claim isidentified in the Application Data Sheet as filed with the presentapplication are hereby incorporated by reference under 37 C.F.R. § 1.57.

BACKGROUND Field

The present application relates to systems, methods, and devices formedical image analysis, diagnosis, risk stratification, decision makingand/or disease tracking.

Description

Coronary heart disease affects over 17.6 million Americans. The currenttrend in treating cardiovascular health issues is generally two-fold.First, physicians generally review a patient’s cardiovascular healthfrom a macro level, for example, by analyzing the biochemistry or bloodcontent or biomarkers of a patient to determine whether there are highlevels of cholesterol elements in the bloodstream of a patient. Inresponse to high levels of cholesterol, some physicians will prescribeone or more drugs, such as statins, as part of a treatment plan in orderto decrease what is perceived as high levels of cholesterol elements inthe bloodstream of the patient.

The second general trend for currently treating cardiovascular healthissues involves physicians evaluating a patient’s cardiovascular healththrough the use of angiography to identify large blockages in variousarteries of a patient. In response to finding large blockages in variousarteries, physicians in some cases will perform an angioplasty procedurewherein a balloon catheter is guided to the point of narrowing in thevessel. After properly positioned, the balloon is inflated to compressor flatten the plaque or fatty matter into the artery wall and/or tostretch the artery open to increase the flow of blood through the vesseland/or to the heart. In some cases, the balloon is used to position andexpand a stent within the vessel to compress the plaque and/or maintainthe opening of the vessel to allow more blood to flow. About 500,000heart stent procedures are performed each year in the United States.

However, a recent federally funded $100 million study calls intoquestion whether the current trends in treating cardiovascular diseaseare the most effective treatment for all types of patients. The recentstudy involved over 5,000 patients with moderate to severe stable heartdisease from 320 sites in 37 countries and provided new evidence showingthat stents and bypass surgical procedures are likely no more effectivethan drugs combined with lifestyle changes for people with stable heartdisease. Accordingly, it may be more advantageous for patients withstable heart disease to forgo invasive surgical procedures, such asangioplasty and/or heart bypass, and instead be prescribed heartmedicines, such as statins, and certain lifestyle changes, such asregular exercise. This new treatment regimen could affect thousands ofpatients worldwide. Of the estimated 500,000 heart stent proceduresperformed annually in the United States, it is estimated that a fifth ofthose are for people with stable heart disease. It is further estimatedthat 25% of the estimated 100,000 people with stable heart disease, orroughly 23,000 people, are individuals that do not experience any chestpain. Accordingly, over 20,000 patients annually could potentially forgoinvasive surgical procedures or the complications resulting from suchprocedures.

To determine whether a patient should forego invasive surgicalprocedures and opt instead for a drug regimen, it can be important tomore fully understand the cardiovascular disease of a patient.Specifically, it can be advantageous to better understand the arterialvessel health of a patient.

SUMMARY

Various embodiments described herein relate to systems, methods, anddevices for medical image analysis, diagnosis, risk stratification,decision making and/or disease tracking.

In particular, in some embodiments, the systems, devices, and methodsdescribed herein are configured to utilize non-invasive medical imagingtechnologies, such as a CT image for example, which can be inputted intoa computer system configured to automatically and/or dynamically analyzethe medical image to identify one or more coronary arteries and/orplaque within the same. For example, in some embodiments, the system canbe configured to utilize one or more machine learning and/or artificialintelligence algorithms to automatically and/or dynamically analyze amedical image to identify, quantify, and/or classify one or morecoronary arteries and/or plaque. In some embodiments, the system can befurther configured to utilize the identified, quantified, and/orclassified one or more coronary arteries and/or plaque to generate atreatment plan, track disease progression, and/or a patient-specificmedical report, for example using one or more artificial intelligenceand/or machine learning algorithms. In some embodiments, the system canbe further configured to dynamically and/or automatically generate avisualization of the identified, quantified, and/or classified one ormore coronary arteries and/or plaque, for example in the form of agraphical user interface. Further, in some embodiments, to calibratemedical images obtained from different medical imaging scanners and/ordifferent scan parameters or environments, the system can be configuredto utilize a normalization device comprising one or more compartments ofone or more materials.

In some embodiments, a normalization device configured to normalize amedical image of a coronary region of a subject for an algorithm-basedmedical imaging analysis comprises: a substrate configured in size andshape to be placed in a medical imager along with a patient so that thenormalization device and the patient can be imaged together such that atleast a region of interest of the patient and the normalization deviceappear in a medical image taken by the medical imager; a plurality ofcompartments positioned on or within the substrate, wherein anarrangement of the plurality of compartments is fixed on or within thesubstrate; a plurality of samples, each of the plurality of samplespositioned within one of the plurality of compartments, and wherein avolume, an absolute density, and a relative density of each of theplurality of samples is known, the plurality of samples comprising: aset of contrast samples, each of the contrast samples comprising adifferent absolute density than absolute densities of the others of thecontrast samples; a set of calcium samples, each of the calcium samplescomprising a different absolute density than absolute densities of theothers of the calcium samples; and a set of fat samples, each of the fatsamples comprising a different absolute density than absolute densitiesof the others of the fat samples; and wherein the set contrast samplesare arranged within the plurality of compartments such that the set ofcalcium samples and the set of fat samples surround the set of contrastsamples.

In some embodiments, the normalization device further comprises anattachment mechanism disposed on the substrate, the attachment mechanismconfigured to attach the normalization device to the patient so that thenormalization device and the patient can be imaged together such thatthe region of interest of the patient and the normalization deviceappear in the medical image taken by the medical imager. In someembodiments of the normalization device, the set of contrast samplescomprise four contrast samples; the set of calcium samples comprise fourcalcium samples; and the set of fat samples comprise four fat samples.In some embodiments of the normalization device, the plurality ofsamples further comprises at least one of an air sample and a watersample. In some embodiments of the normalization device, the volume of afirst contrast sample is different than a volume of a second contrastsample; the volume of a first calcium sample is different than a volumeof a second calcium sample; and the volume of a first fat sample isdifferent than a volume of a second fat sample. In some embodiments ofthe normalization device, a first contrast sample is arranged within theplurality of compartments so as to be adjacent to a second contrastsample, a first calcium sample, and a first fat sample. In someembodiments of the normalization device, a first calcium sample isarranged within the plurality of compartments so as to be adjacent to asecond calcium sample, a first contrast sample, and a first fat sample.In some embodiments of the normalization device, a first fat sample isarranged within the plurality of compartments so as to be adjacent to asecond fat sample, a first contrast sample, and a first calcium sample.In some embodiments of the normalization device, the set of contrastsamples, the set of calcium samples, and the set of fat samples arearranged in a manner that mimics a blood vessel.

In some embodiments, a computer implemented method for generating a riskassessment of atherosclerotic cardiovascular disease (ASCVD) using thenormalization device, wherein normalization of the medical imagingimproves accuracy of the algorithm-based imaging analysis, comprises:receiving a first set of images of a first arterial bed and a first setof images of a second arterial bed, the second arterial bed beingnoncontiguous with the first arterial bed, and wherein at least one ofthe first set of images of the first arterial bed and the first set ofimages of the second arterial bed are normalized using the normalizationdevice; quantifying ASCVD in the first arterial bed using the first setof images of the first arterial bed; quantifying ASCVD in the secondarterial bed using the first set of images of the second arterial bed;and determining a first ASCVD risk score based on the quantified ASCVDin the first arterial bed and the quantified ASCVD in the secondarterial bed.

In some embodiments, the method for generating a risk assessment ofatherosclerotic cardiovascular disease (ASCVD) further comprises:determining a first weighted assessment of the first arterial bed basedon the quantified ASCVD of the first arterial bed and weighted adverseevents for the first arterial bed; and determining a second weightedassessment of the second arterial bed based on the quantified ASCVD ofthe second arterial bed and weighted adverse events for the secondarterial bed, wherein determining the first ASCVD risk score furthercomprises determining the ASCVD risk score based on the first weightedassessment and the second weighted assessment. Further, in someembodiments, the method for generating a risk assessment ofatherosclerotic cardiovascular disease (ASCVD) further comprises:receiving a second set of images of the first arterial bed and a secondset of images of the second arterial bed, the second set of images ofthe first arterial bed generated subsequent to generating the first setof image of the first arterial bed, and the second set of images of thesecond arterial bed generated subsequent to generating the first set ofimage of the second arterial bed; quantifying ASCVD in the firstarterial bed using the second set of images of the first arterial bed;quantifying ASCVD in the second arterial bed using the second set ofimages of the second arterial bed; and determining a second ASCVD riskscore based on the quantified ASCVD in the first arterial bed using thesecond set of images, and the quantified ASCVD in the second arterialbed using the second set of images. In some embodiments of the methodfor generating a risk assessment of atherosclerotic cardiovasculardisease (ASCVD), determining the second ASCVD risk score is furtherbased on the first ASCVD risk score. In some embodiments of the methodfor generating a risk assessment of atherosclerotic cardiovasculardisease (ASCVD), the first arterial bed includes arteries of one of theaorta, carotid arteries, lower extremity arteries, renal arteries, orcerebral arteries, and wherein the second arterial bed includes arteriesof one of the aorta, carotid arteries, lower extremity arteries, renalarteries, or cerebral arteries that are different than the arteries ofthe first arterial bed.

In some embodiments, a computer implemented method of generating amulti-media medical report for a patient that is based on imagesgenerated using the normalization device, wherein the normalizationdevice improves accuracy of the non-invasive medical image analysis, themedical report associated with one or more tests of the patient,comprises: receiving an input of a request to generate the medicalreport for a patient, the request indicating a format for the medicalreport; receiving patient information relating to the patient, thepatient information associated with the report generation request;determining one or more patient characteristics associated with thepatient using the patient information; accessing associations betweentypes of medical reports and patient medical information, wherein thepatient medical information includes medical images relating to thepatient and test results of one or more test that were performed on thepatient, the medical images generated using the normalization device;accessing report content associated with the patient’s medicalinformation and the medical report requested, wherein the report contentcomprises multimedia content that is not related to a specific patient,the multimedia content including a greeting segment in the language ofthe patient, an explanation segment explaining a type of test conducted,a results segment for conveying test results, and an explanation segmentexplaining results of the test, and a conclusion segment, wherein atleast a portion of the multimedia content includes a test result and oneor more medical images that are related to a test performed on thepatient; and generating, based at least in part on the format of themedical report, the requested medical report using the patientinformation and report content.

In some embodiments, a computer implemented method of assessing a riskof coronary artery disease (CAD) for a subject by generating one or moreCAD risk scores for the subject based on multi-dimensional informationderived from non-invasive medical image analysis using the normalizationdevice, wherein the normalization device improves accuracy of thenon-invasive medical image analysis, comprises: accessing, by a computersystem, a medical image of a coronary region of a subject, wherein themedical image of the coronary region of the subject is obtainednon-invasively; identifying, by the computer system, one or moresegments of coronary arteries within the medical image of the coronaryregion of the subject; determining, by the computer system, for each ofthe identified one or more segments of coronary arteries one or moreplaque parameters, vessel parameters, and clinical parameters, whereinthe one or more plaque parameters comprise one or more of plaque volume,plaque composition, plaque attenuation, or plaque location, wherein theone or more vessel parameters comprise one or more of stenosis severity,lumen volume, percentage of coronary blood volume, or percentage offractional myocardial mass, and wherein the one or more clinicalparameters comprise one or more of percentile health condition for ageor percentile health condition for gender; generating, by the computersystem, for each of the identified one or more segments of coronaryarteries a weighted measure of the determined one or more plaqueparameters, vessel parameters, and clinical parameters, wherein theweighted measure is generated by applying a correction factor;combining, by the computer system, the generated weighted measure of thedetermined one or more plaque parameters, vessel parameters, andclinical parameters for each of the identified one or more segments ofcoronary arteries to generate one or more per-vessel, per-vascularterritory, or per-subject CAD risk scores; and generating, by thecomputer system, a graphical plot of the generated one or moreper-vessel, per-vascular territory, or per-subject CAD risk scores forvisualizing and quantifying risk of CAD for the subject on one or moreof a per-vessel, per-vascular, or per-subject basis, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

In some embodiments, a computer implemented method of tracking efficacyof a medical treatment for a plaque-based disease based on non-invasivemedical image analysis using the normalization device, wherein thenormalization device improves accuracy of the non-invasive medical imageanalysis, comprises: accessing, by a computer system, a first set ofplaque parameters and a first set of vascular parameters associated witha subject, wherein the first set of plaque parameters and the first setof vascular parameters are derived from a first medical image of thesubject comprising one or more regions of plaque, wherein the firstmedical image of the subject is obtained non-invasively at a first pointin time, wherein the first set of plaque parameters comprises one ormore of density, location, or volume of one or more regions of plaquefrom the medical image of the subject at the first point in time, andwherein the first set of vascular parameters comprises vascularremodeling of a vasculature at the first point in time; accessing, bythe computer system, a second medical image of the subject, wherein thesecond medical image of the subject is obtained non-invasively at asecond point in time after the subject is treated with a medicaltreatment, the second point in time being later than the first point intime, wherein the second medical image of the subject comprises the oneor more regions of plaque; identifying, by the computer system, the oneor more regions of plaque from the second medical image; determining, bythe computer system, a second set of plaque parameters and a second ofvascular parameters associated with the subject by analyzing the one ormore regions of plaque from the second medical image, wherein the secondset of plaque parameters comprises one or more of density, location, orvolume of the one or more regions of plaque from the medical image ofthe subject at the second point in time, and wherein the second set ofvascular parameters comprises vascular remodeling of the vasculature atthe second point in time; analyzing, by the computer system, one or morechanges between the first set of plaque parameters and the second set ofplaque parameters; analyzing, by the computer system, one or morechanges between the first set of vascular parameters and the second setof vascular parameters; tracking, by the computer system, progression ofthe plaque-based disease based on one or more of the analyzed one ormore changes between the first set of plaque parameters and the secondset of plaque parameters or the analyzed one or more changes between thefirst set of vascular parameters and the second set of vascularparameters; and determining, by the computer system, efficacy of themedical treatment based on the tracked progression of the plaque-baseddisease, wherein the computer system comprises a computer processor andan electronic storage medium.

In some embodiments, a computer implemented method of determiningcontinued personalized treatment for a subject with atheroscleroticcardiovascular disease (ASCVD) risk based on coronary CT angiography(CCTA) analysis using one or more quantitative imaging algorithms usingthe normalization device, wherein the normalization device improvesaccuracy of the one or more quantitative imaging algorithms, comprises:assessing, by a computer system, a baseline ASCVD risk of the subject byanalyzing baseline CCTA analysis results using one or more quantitativeimaging algorithms, the baseline CCTA analysis results based at least inpart on one or more atherosclerosis parameters or perilesional tissueparameters, the one or more atherosclerosis parameters comprising one ormore of presence, locality, extent, severity, or type ofatherosclerosis; categorizing, by the computer system, the baselineASCVD risk of the subject into one or more predetermined categories ofASCVD risk; determining, by the computer system, an initial personalizedproposed treatment for the subject based at least in part on thecategorized baseline ASCVD risk of the subject, the initial personalizedproposed treatment for the subject comprising one or more of medicaltherapy, lifestyle therapy, or interventional therapy; assessing, by thecomputer system, subject response to the determined initial personalizedproposed treatment by subsequent CCTA analysis using one or morequantitative imaging algorithms and comparing the subsequent CCTAanalysis results to the baseline CCTA analysis results, the subsequentCCTA analysis performed after applying the determined initialpersonalized proposed treatment to the subject, wherein the subjectresponse is assessed based on one or more of progression, stabilization,or regression of ASCVD; and determining, by the computer system, acontinued personalized proposed treatment for the subject based at leastin part on the assessed subject response, the continued personalizedproposed treatment comprising a higher tiered approach than the initialpersonalized proposed treatment when the assessed subject responsecomprises progression of ASCVD, the continued personalized proposedtreatment comprising one or more of medical therapy, lifestyle therapy,or interventional therapy, wherein the computer system comprises acomputer processor and an electronic storage medium.

In some embodiments, a computer implemented method of determiningvolumetric stenosis severity in the presence of atherosclerosis based onnon-invasive medical image analysis for risk assessment of coronaryartery disease (CAD) for a subject using the normalization device,wherein the normalization device improves accuracy of the non-invasivemedical image analysis, comprises: accessing, by a computer system, amedical image of a coronary region of a subject, wherein the medicalimage of the coronary region of the subject is obtained non-invasively;identifying, by the computer system, one or more segments of coronaryarteries and one or more regions of plaque within the medical image ofthe coronary region of the subject; determining, by the computer system,for the identified one or more segments of coronary arteries a lumenwall boundary in the presence of the one or more regions of plaque and ahypothetical normal artery boundary in case the one or more regions ofplaque were not present, wherein the determined lumen wall boundary andthe hypothetical normal artery boundary comprise tapering of the one ormore segments of coronary arteries, and wherein the determined lumenwall boundary further comprises a boundary of the one or more regions ofplaque; quantifying, by the computer system, for the identified one ormore segments of coronary arteries a lumen volume based on thedetermined lumen wall boundary, wherein the quantified lumen volumetakes into account the tapering of the one or more segments of coronaryarteries and the boundary of the one or more regions of plaque;quantifying, by the computer system, for the identified one or moresegments of coronary arteries a hypothetical normal vessel volume basedon the determined hypothetical normal artery boundary, wherein thequantified hypothetical normal vessel volume takes into account thetapering of the one or more segments of coronary arteries; determining,by the computer system, for the identified one or more segments ofcoronary arteries volumetric stenosis by determining a percentage orratio of the quantified lumen volume compared to the hypothetical normalvessel volume; and determining, by the computer system, a risk of CADfor the subject based at least in part on the determined volumetricstenosis for the identified one or more segments of coronary arteries,wherein the computer system comprises a computer processor and anelectronic storage medium.

In some embodiments, a computer implemented method of quantifyingischemia for a subject based on non-invasive medical image analysisusing the normalization device, wherein the normalization deviceimproves accuracy of the non-invasive medical image analysis, comprises:accessing, by a computer system, a medical image of a coronary region ofa subject, wherein the medical image of the coronary region of thesubject is obtained non-invasively; identifying, by the computer system,one or more segments of coronary arteries and one or more regions ofplaque within the medical image of the coronary region of the subject;quantifying, by the computer system, a proximal volume of a proximalsection and a distal volume of a distal section along the one or moresegments of coronary arteries, wherein the proximal section does notcomprise the one or more regions of plaque, and wherein the distalsection comprises at least one of the one or more regions of plaque;accessing, by the computer system, an assumed velocity of blood flow atthe proximal section; quantifying, by the computer system, a velocity ofblood flow at the distal section based at least in part on the assumedvelocity of blood flow at the proximal section, the quantified proximalvolume of the proximal section, and the distal volume of the distalsection along the one or more segments of coronary arteries;determining, by the computer system, a velocity time integral of bloodflow at the distal section based at least in part on the quantifiedvelocity of blood flow at the distal section; and quantifying, by thecomputer system, ischemia along the one or more segments of coronaryarteries based at least in part on the determined velocity time integralof blood flow at the distal section, wherein the computer systemcomprises a computer processor and an electronic storage medium.

For purposes of this summary, certain aspects, advantages, and novelfeatures of the invention are described herein. It is to be understoodthat not necessarily all such advantages may be achieved in accordancewith any particular embodiment of the invention. Thus, for example,those skilled in the art will recognize that the invention may beembodied or carried out in a manner that achieves one advantage or groupof advantages as taught herein without necessarily achieving otheradvantages as may be taught or suggested herein.

All of these embodiments are intended to be within the scope of theinvention herein disclosed. These and other embodiments will becomereadily apparent to those skilled in the art from the following detaileddescription having reference to the attached figures, the invention notbeing limited to any particular disclosed embodiment(s).

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed aspects will hereinafter be described in conjunction withthe accompanying drawings, which are incorporated in and constitute apart of this specification, and are provided to illustrate and provide afurther understanding of example embodiments, and not to limit thedisclosed aspects. In the drawings, like designations denote likeelements unless otherwise stated.

FIG. 1 is a flowchart illustrating an overview of an exampleembodiment(s) of a method for medical image analysis, visualization,risk assessment, disease tracking, treatment generation, and/or patientreport generation.

FIG. 2A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for analysis and classification of plaque froma medical image.

FIG. 2B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of non-calcified plaque froma non-contrast CT image(s).

FIG. 3A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for risk assessment based on medical imageanalysis.

FIG. 3B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for quantification of atherosclerosis based onmedical image analysis.

FIG. 3C is a flowchart illustrating an overview of an exampleembodiment(s) of a method for quantification of stenosis and generationof a CAD-RADS score based on medical image analysis.

FIG. 3D is a flowchart illustrating an overview of an exampleembodiment(s) of a method for disease tracking based on medical imageanalysis.

FIG. 3E is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of cause of change incalcium score based on medical image analysis.

FIG. 4A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for prognosis of a cardiovascular event basedon medical image analysis.

FIG. 4B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of patient-specific stentparameters based on medical image analysis.

FIG. 5A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for generation of a patient-specific medicalreport based on medical image analysis.

FIGS. 5B-5I illustrate example embodiment(s) of a patient-specificmedical report generated based on medical image analysis.

FIG. 6A illustrates an example of a user interface that can be generatedand displayed on the system, the user interface having multiple panels(views) that can show various corresponding views of a patient’sarteries.

FIG. 6B illustrates an example of a user interface that can be generatedand displayed on the system, the user interface having multiple panelsthat can show various corresponding views of a patient’s arteries.

FIGS. 6C, 6D, and 6E illustrate certain details of a multiplanarreformat (MPR) vessel view in the second panel, and certainfunctionality associated with this view.

FIG. 6F illustrates an example of a three-dimensional (3D) rendering ofa coronary artery tree that allows a user to view the vessels and modifythe labels of a vessel.

FIG. 6G illustrates an example of a panel of the user interface thatprovides shortcut commands that a user may employ while analyzinginformation in the user interface in a coronary artery tree view, anaxial view, a sagittal view, and a coronal view.

FIG. 6H illustrates examples of panels of the user interface for viewingDICOM images in three anatomical planes: axial, coronal, and sagittal.

FIG. 6I illustrates an example of a panel of the user interface showinga cross-sectional view of a vessel, in the graphical overlay of anextracted feature of the vessel.

FIG. 6J illustrates an example of a toolbar that allows a user to selectdifferent vessels for review and analysis.

FIG. 6K illustrates an example of a series selection panel of the userinterface in an expanded view of the toolbar illustrated in FIG. 6J,which allows a user to expand the menu to view all the series (set ofimages) that are available for review and analysis for a particularpatient.

FIG. 6L illustrates an example of a selection panel that can bedisplayed on the user interface that may be uses to select a vesselsegment for analysis.

FIG. 6M illustrates an example of a panel that can be displayed on theuser interface to add a new vessel on the image.

FIG. 6N illustrates examples of two panels that can be displayed on theuser interface to name, or to rename, a vessel in the 3-D artery treeview.

FIG. 7A illustrates an example of an editing toolbar which allows usersto modify and improve the accuracy of the findings resulting fromprocessing CT scans with a machine learning algorithm and then by ananalyst.

FIGS. 7B and 7C illustrate examples of certain functionality of thetracker tool.

FIGS. 7D and 7E illustrate certain functionality of the vessel and lumenwall tools, which are used to modify the lumen and vessel wall contours.

FIG. 7F illustrates the lumen snap tool button (left) in the vessel snaptool button (right) on a user interface which can be used to activatethese tools.

FIG. 7G illustrates an example of a panel that can be displayed on theuser interface while using the lumen snap tool in the vessel snap tool.

FIG. 7H illustrates an example of a panel of the user interface that canbe displayed while using the segment tool which allows for marking theboundaries between individual coronary segments on the MPR.

FIG. 7I illustrates an example of a panel of the user interface thatallows a different name to be selected for a segment.

FIG. 7J illustrates an example of a panel of the user interface that canbe displayed while using the stenosis tool, which allows a user toindicate markers to mark areas of stenosis on a vessel.

FIG. 7K illustrates an example of a stenosis button of the userinterface which can be used to drop five evenly spaced stenosis markers.

FIG. 7L illustrates an example of a stenosis button of the userinterface which can be used to drop stenosis markers based on the useredited lumen and vessel wall contours.

FIG. 7M illustrates the stenosis markers on segments on a curvedmultiplanar vessel (CMPR) view.

FIG. 7N illustrates an example of a panel of the user interface that canbe displayed while using the plaque overlay tool.

FIGS. 7O and 7P illustrate a button on the user interface that can beselected to the plaque thresholds.

FIG. 7Q illustrates a panel of the user interface which can receive auser input to adjust plaque threshold levels for low-density plaque,non-calcified plaque, and calcified plaque.

FIG. 7R illustrates a cross-sectional view of a vessel indicating areasof plaque which are displayed in the user interface in accordance withthe plaque thresholds.

FIG. 7S illustrates a panel can be displayed showing plaque thresholdsin a vessel statistics panel that includes information on the vesselbeing viewed.

FIG. 7T illustrates a panel showing a cross-sectional view of a vesselthat can be displayed while using the centerline tool, which allowsadjustment of the center of the lumen.

FIGS. 7U, 7V, 7W illustrate examples of panels showing other views of avessel that can be displayed when using the centerline tool. FIG. 7U isan example of a view that can be displayed when extending the centerlineof a vessel. FIG. 7V illustrates an example of a view that can bedisplayed when saving or canceling centerline edits. FIG. 7W is anexample of a CMPR view that can be displayed when editing the vesselcenterline.

FIG. 7X illustrates an example of a panel that can be displayed whileusing the chronic total occlusion (CTO) tool, which is used to indicatea portion of artery with 100% stenosis and no detectable blood flow.

FIG. 7Y illustrates an example of a panel that can be displayed whileusing the stent tool, which allows a user to mark the extent of a stentin a vessel.

FIGS. 7Z and 7AAillustrates examples of panels that can be displayedwhile using the exclude tool, which allows a portion of the vessel to beexcluded from the analysis, for example, due to image aberrations. A row

FIGS. 7AB and 7AC illustrate examples of additional panels that can bedisplayed while using the exclude tool. FIG. 7 AB illustrates a panelthat can be used to add a new exclusion. FIG. 7AC illustrates a panelthat can be used to add a reason for the exclusion.

FIGS. 7AD, 7AE, 7AF, and 7AG illustrate examples of panels that can bedisplayed while using the distance tool, which can be used to measurethe distance between two points on an image. For example, FIG. 7ADillustrates the distance tool being used to measure a distance on anSMPR view. FIG. 7AE illustrates the distance tool being used to measurea distance on an CMPR view. FIG. 7AF illustrates the distance will beused to measure a distance on a cross-sectional view of the vessel. FIG.7AG illustrates the distance tool being used to measure a distance on anaxial view.

FIG. 7AH illustrates a “vessel statistics” portion (button) of a panelwhich can be selected to display the vessel statistics tab.

FIG. 7AI illustrates the vessel statistics tab.

FIG. 7AJ illustrates functionality on the vessel statistics tab thatallows a user to click through the details of multiple lesions.

FIG. 7AK further illustrates an example of the vessel panel which theuser can use to toggle between vessels.

FIG. 8A illustrates an example of a panel of the user interface thatshows stenosis, atherosclerosis, and CAD-RADS results of the analysis.

FIG. 8B illustrates an example of a portion of a panel displayed on theuser interface that allows selection of a territory or combination ofterritories (e.g., left main artery (LM), left anterior descendingartery (LAD), left circumflex artery (LCx), right coronary artery (RCA),according to various embodiments.

FIG. 8C illustrates an example of a panel that can be displayed on theuser interface showing a cartoon representation of a coronary arterytree (“cartoon artery tree”).

FIG. 8D illustrates an example of a panel that can be displayed on theuser interface illustrating territory selection using the cartoon arterytree.

FIG. 8E illustrates an example panel that can be displayed on the userinterface showing per-territory summaries.

FIG. 8F illustrates an example panel that can be displayed on the userinterface showing a SMPR view of a selected vessel, and correspondingstatistics of the selected vessel.

FIG. 8G illustrates an example of a portion of a panel that can bedisplayed in the user interface indicating the presence of a stent,which is displayed at the segment level.

FIG. 8H illustrates an example of a portion of a panel that can bedisplayed in the user interface indicating CTO presence at the segmentlevel.

FIG. 8I illustrates an example of a portion of a panel that can bedisplayed in the user interface indicating left or right dominance ofthe patient.

FIG. 8J illustrates an example of a panel that can be displayed on theuser interface showing cartoon artery tree with indications of anomaliesthat were found.

FIG. 8K illustrates an example of a portion of a panel that can bedisplayed on the panel of FIG. 8J that can be selected to show detailsof an anomaly.

FIG. 9A illustrates an example of an atherosclerosis panel that can bedisplayed on the user interface which displays a summary ofatherosclerosis information based on the analysis.

FIG. 9B illustrates an example of a vessel selection panel which can beused to select a vessel such that the summary of atherosclerosisinformation is displayed on a per segment basis.

FIG. 9C illustrates an example of a panel that can be displayed on theuser interface which shows per segment atherosclerosis information.

FIG. 9D illustrates an example of a panel that can be displayed on theuser interface that contains stenosis per patient data.

FIG. 9E illustrates an example of a portion of a panel that can bedisplayed on the user interface that when a count is selected (e.g., byhovering over the number) segment details are displayed.

FIG. 9F illustrates an example of a portion of a panel that can bedisplayed on the user interface that shows stenosis per segment in agraphical format, for example, in a stenosis per segment bar graph.

FIG. 9G illustrates another example of a panel that can be displayed onthe user interface showing information of the vessel, for example,diameter stenosis and minimum luminal diameter.

FIG. 9H illustrates an example of a portion of a panel that can bedisplayed on the user interface indicating a diameter stenosis legend.

FIG. 9I illustrates an example of a panel that can be displayed on theuser interface indicating minimum and reference lumen diameters.

FIG. 9J illustrates a portion of the panel shown in FIG. 9I, and showshow specific minimum lumen diameter details can be quickly andefficiently displayed by selecting (e.g., by hovering over) a desiredgraphic of a lumen.

FIG. 9K illustrates an example of a panel that can be displayed in userinterface indicating CADS-RADS score selection.

FIG. 9L illustrates an example of a panel that can be displayed in theuser interface showing further CAD-RADS details generated in theanalysis.

FIG. 9M illustrates an example of a panel that can be displayed in theuser interface showing a table indicating quantitative stenosis andvessel outputs which are determined during the analysis.

FIG. 9N illustrates an example of a panel that can be displayed in theuser interface showing a table indicating quantitative plaque outputs.

FIG. 10 is a flowchart illustrating a process 1000 for analyzing anddisplaying CT images and corresponding information.

FIGS. 11A and 11B are example CT images illustrating how plaque canappear differently depending on the image acquisition parameters used tocapture the CT images. FIG. 11A illustrates a CT image reconstructedusing filtered back projection, while FIG. 11B illustrates the same CTimage reconstructed using iterative reconstruction.

FIGS. 11C and 11D provide another example that illustrates that plaquecan appear differently in CT images depending on the image acquisitionparameters used to capture the CT images. FIG. 11C illustrates a CTimage reconstructed by using iterative reconstruction, while FIG. 11Dillustrates the same image reconstructed using machine learning.

FIG. 12A is a block diagram representative of an embodiment of anormalization device that can be configured to normalize medical imagesfor use with the methods and systems described herein.

FIG. 12B is a perspective view of an embodiment of a normalizationdevice including a multilayer substrate.

FIG. 12C is a cross-sectional view of the normalization device of FIG.12B illustrating various compartments positioned therein for holdingsamples of known materials for use during normalization.

FIG. 12D illustrates a top down view of an example arrangement of aplurality of compartments within a normalization device. In theillustrated embodiment, the plurality of compartments are arranged in arectangular or grid-like pattern.

FIG. 12E illustrates a top down view of another example arrangement of aplurality of compartments within a normalization device. In theillustrated embodiment, the plurality of compartments are arranged in acircular pattern.

FIG. 12F is a cross-sectional view of another embodiment of anormalization device illustrating various features thereof, includingadjacently arranged compartments, self-sealing fillable compartments,and compartments of various sizes.

FIG. 12G is a perspective view illustrating an embodiment of anattachment mechanism for a normalization device that uses hook and loopfasteners to secure a substrate of the normalization device to afastener of the normalization device.

FIGS. 12H and 12I illustrate an embodiment of a normalization devicethat includes an indicator configured to indicate an expiration statusof the normalization device.

FIG. 12J is a flowchart illustrating an example method for normalizingmedical images for an algorithm-based medical imaging analysis, whereinnormalization of the medical images improves accuracy of thealgorithm-based medical imaging analysis.

FIG. 13 is a block diagram depicting an embodiment(s) of a system formedical image analysis, visualization, risk assessment, diseasetracking, treatment generation, and/or patient report generation.

FIG. 14 is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of a system for medical image analysis, visualization, riskassessment, disease tracking, treatment generation, and/or patientreport generation.

FIG. 15 illustrates an embodiment of a normalization device.

FIG. 16 is a system diagram which shows various components of an exampleof a system for automatically generating patient medical reports, forexample, patient medical reports based on CT scans and analysis,utilizing certain systems and methods described herein.

FIG. 17 is a block diagram that shows an example of data flowfunctionality for generating the patient medical report based on one ormore scans of the patient, patient information, medical practitioner’sanalysis of the scans, and/or previous test results.

FIG. 18A is a block diagram of a first portion of a process forgenerating medical report using the functionality and data described inreference to FIG. 2 , according to some embodiments.

FIG. 18B is a block diagram of a second portion of a process forgenerating medical report using the functionality and data described inreference to FIG. 2 , according to some embodiments.

FIG. 18C is a block diagram of a third portion of a process forgenerating medical report using the functionality and data described inreference to FIG. 2 , according to some embodiments.

FIG. 18D is a diagram illustrating various portions that can make up themedical report, and input can be provided by the medical practitionerand by patient information or patient input.

FIG. 18E is a schematic illustrating an example of a medical reportgeneration data flow and communication of data used to generate areport.

FIG. 18F is a diagram illustrating multiple structures for storinginformation that is used in a medical report, the information associatedwith a patient based on one or more characteristics of the patient, thepatient’s medical condition, and/or the input from the patient or amedical practitioner.

FIG. 19A illustrates an example of a process for determining a riskassessment using sequential imaging of noncontiguous arterial beds of apatient, according to some embodiments.

FIG. 19B illustrates an example where sequential noncontiguous arterialbed imaging is performed for the coronary arteries.

FIG. 19C is an example of a process for determining a risk assessmentusing sequential imaging of non-contiguous arterial beds, according tosome embodiments.

FIG. 19D is an example of a process for determining a risk assessmentusing sequential imaging of non-contiguous arterial beds, according tosome embodiments.

FIG. 19E is a block diagram depicting an embodiment of a computerhardware system configured to run software for implementing one or moreembodiments of systems and methods for determining a risk assessmentusing sequential imaging of noncontiguous arterial beds of a patient.

FIG. 20A illustrates one or more features of an example ischemicpathway.

FIG. 20B is a block diagram depicting one or more contributors and oneor more temporal sequences of consequences of ischemia utilized by anexample embodiment(s) described herein.

FIG. 20C is a block diagram depicting one or more features of an exampleembodiment(s) for determining ischemia by weighting different factorsdifferently.

FIG. 20D is a block diagram depicting one or more features of an exampleembodiment(s) for calculating a global ischemia index.

FIG. 20E is a flowchart illustrating an overview of an exampleembodiment(s) of a method for generating a global ischemia index for asubject and using the same to assist assessment of risk of ischemia forthe subject.

FIG. 21 is a flowchart illustrating an overview of an exampleembodiment(s) of a method for generating a coronary artery disease (CAD)Score(s) for a subject and using the same to assist assessment of riskof CAD for the subject.

FIG. 22A illustrates an example(s) of tracking the attenuation of plaquefor analysis and/or treatment of coronary artery and/or other vasculardisease.

FIG. 22B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for treating to the image.

FIG. 23A illustrates an example embodiment(s) of systems and methods fordetermining treatments for reducing cardiovascular risk and/or events.

FIGS. 23B-C illustrate an example embodiment(s) of definitions orcategories of atherosclerosis severity used by an example embodiment(s)of systems and methods for determining treatments for reducingcardiovascular risk and/or events.

FIG. 23D illustrates an example embodiment(s) of definitions orcategories of disease progression, stabilization, and/or regression usedby an example embodiment(s) of systems and methods for determiningtreatments for reducing cardiovascular risk and/or events.

FIG. 23E illustrates an example embodiment(s) of a time-to-treatmentgoal(s) for an example embodiment(s) of systems and methods fordetermining treatments for reducing cardiovascular risk and/or events.

FIGS. 23F-G illustrate an example embodiment(s) of a treatment(s)employing lipid lowering medication(s) and/or treatment(s) generated byan example embodiment(s) of systems and methods for determiningtreatments for reducing cardiovascular risk and/or events.

FIGS. 23H-I illustrate an example embodiment(s) of a treatment(s)employing diabetic medication(s) and/or treatment(s) generated by anexample embodiment(s) of systems and methods for determining treatmentsfor reducing cardiovascular risk and/or events.

FIG. 23J is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determining treatments for reducingcardiovascular risk and/or events.

FIG. 24A is a schematic illustration of an artery.

FIG. 24B illustrates an embodiment(s) of determining percentage stenosisand remodeling index.

FIG. 24C is a schematic illustration of an artery.

FIG. 24D is a schematic illustration of an artery with longatherosclerotic regions of plaque.

FIG. 24E is a example illustrating how an inaccurately estimated R0 cansignificantly affect the resulting percent stenosis and/or remodelingindex.

FIG. 24F is a schematic illustration of lumen diameter v. outer walldiameter.

FIG. 24G is a schematic illustration of calculation of an estimatedreference diameter(s) along a vessel where plaque is present.

FIG. 24H is a schematic illustration of an embodiment(s) of determiningvolumetric stenosis.

FIG. 24I is a schematic illustration of an embodiment(s) of determiningvolumetric stenosis.

FIG. 24J is a schematic illustration of an embodiment(s) of determiningvolumetric remodeling.

FIG. 24K illustrates an embodiment(s) of coronary vessel blood volumeassessment based on total coronary volume.

FIG. 24L illustrates an embodiment(s) of coronary vessel blood volumeassessment based on territory or artery-specific volume.

FIG. 24M illustrates an embodiment(s) of coronary vessel blood volumeassessment based on within-artery % fractional blood volume.

FIG. 24N illustrates an embodiment(s) of assessment of coronary vesselblood volume.

FIG. 24O illustrates an embodiment(s) of assessment of % vessel volumestenosis as a measure of ischemia.

FIG. 24P illustrates an embodiment(s) of assessment of pressuredifference across a lesion as a measure of ischemia.

FIG. 24Q illustrates an embodiment(s) of application of the continuityequation to coronary arteries.

FIG. 24R is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determining volumetric stenosis and/orvolumetric vascular remodeling.

FIG. 24S is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determining ischemia.

FIG. 25A is a flowchart illustrating a process for determining anindicator of risk that an atherosclerotic lesion will contribute to amyocardial infarction or other major adverse cardiovascular event.

FIG. 25B is schematic illustration of a human heart, illustratingcertain coronary arteries.

FIG. 25C is a flowchart illustrating a process for determining anindicator of a myocardial risk posed by an atherosclerotic lesion.

FIG. 25D is a flowchart illustrating a process for determining anindicator of a segmental myocardial risk posed by an atheroscleroticlesion.

FIG. 25E is a flowchart illustrating a process for determining a risk ofadverse clinical events caused by an atherosclerotic lesion.

FIG. 25F is a flowchart illustrating a process for updating a risk ofadverse clinical events caused by an atherosclerotic lesion.

FIG. 25G is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of systems, devices, and methods for determining amyocardial risk factor from image-based quantification andcharacterizations of coronary atherosclerosis, vascular morphology, andmyocardium.

FIG. 26 is a flowchart illustrating a process for analyzing a CFD-basedindication of ischemia using a characterization of atherosclerosis andvascular morphology.

FIG. 27A is a block diagram illustrating an example embodiment(s) ofsystems, devices, and methods for determining patient-specific and/orsubject-specific coronary artery disease (CAD) risk factor goals fromimage-based quantified phenotyping of atherosclerosis.

FIG. 27B is a block diagram of an example of a computing system that canbe used to implement the systems, processes, and methods describedherein relating to functionality described in reference to FIG. 27A.

FIGS. 28A-28B illustrate an example embodiment of identification ofcoronary and aortic disease / atherosclerosis identified on a coronaryCT angiogram (CCTA) utilizing embodiments of the systems, devices, andmethods described herein.

FIG. 28C is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for image-based diagnosis, riskassessment, and/or characterization of a major adverse cardiovascularevent.

FIG. 28D is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for image-based diagnosis, riskassessment, and/or characterization of a major adverse cardiovascularevent.

FIG. 28E is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for image-based diagnosis, riskassessment, and/or characterization of a major adverse cardiovascularevent.

FIG. 28F is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of systems, devices, and methods described herein.

FIG. 29A is a block diagram illustrating an example embodiment of asystem, device, and method for improving the accuracy of CADmeasurements in non-invasive imaging; and

FIG. 29B is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for improving the accuracy ofCAD measurements in non-invasive imaging.

FIG. 30A is a block diagram illustrating an example embodiment of asystem, device, and method for longitudinal image-based phenotyping toenhance drug discovery or development.

FIG. 30B is a block diagram depicting an embodiment of a computerhardware system configured to run software for implementing one or moreembodiments of systems, devices, and methods for determiningpatient-specific coronary artery disease (CAD) risk factor goals fromimage-based quantification and characterization of coronaryatherosclerosis burden, type, and/or rate of progression.

DETAILED DESCRIPTION

Although several embodiments, examples, and illustrations are disclosedbelow, it will be understood by those of ordinary skill in the art thatthe inventions described herein extend beyond the specifically disclosedembodiments, examples, and illustrations and includes other uses of theinventions and obvious modifications and equivalents thereof.Embodiments of the inventions are described with reference to theaccompanying figures, wherein like numerals refer to like elementsthroughout. The terminology used in the description presented herein isnot intended to be interpreted in any limited or restrictive mannersimply because it is being used in conjunction with a detaileddescription of certain specific embodiments of the inventions. Inaddition, embodiments of the inventions can comprise several novelfeatures and no single feature is solely responsible for its desirableattributes or is essential to practicing the inventions hereindescribed.

Introduction

Disclosed herein are systems, methods, and devices for medical imageanalysis, diagnosis, risk stratification, decision making and/or diseasetracking. Coronary heart disease affects over 17.6 million Americans.The current trend in treating cardiovascular health issues is generallytwo-fold. First, physicians generally review a patient’s cardiovascularhealth from a macro level, for example, by analyzing the biochemistry orblood content or biomarkers of a patient to determine whether there arehigh levels of cholesterol elements in the bloodstream of a patient. Inresponse to high levels of cholesterol, some physicians will prescribeone or more drugs, such as statins, as part of a treatment plan in orderto decrease what is perceived as high levels of cholesterol elements inthe bloodstream of the patient.

The second general trend for currently treating cardiovascular healthissues involves physicians evaluating a patient’s cardiovascular healththrough the use of angiography to identify large blockages in variousarteries of a patient. In response to finding large blockages in variousarteries, physicians in some cases will perform an angioplasty procedurewherein a balloon catheter is guided to the point of narrowing in thevessel. After properly positioned, the balloon is inflated to compressor flatten the plaque or fatty matter into the artery wall and/or tostretch the artery open to increase the flow of blood through the vesseland/or to the heart. In some cases, the balloon is used to position andexpand a stent within the vessel to compress the plaque and/or maintainthe opening of the vessel to allow more blood to flow. About 500,000heart stent procedures are performed each year in the United States.

However, a recent federally funded $100 million study calls intoquestion whether the current trends in treating cardiovascular diseaseare the most effective treatment for all types of patients. The recentstudy involved over 5,000 patients with moderate to severe stable heartdisease from 320 sites in 37 countries and provided new evidence showingthat stents and bypass surgical procedures are likely no more effectivethan drugs combined with lifestyle changes for people with stable heartdisease. Accordingly, it may be more advantageous for patients withstable heart disease to forgo invasive surgical procedures, such asangioplasty and/or heart bypass, and instead be prescribed heartmedicines, such as statins, and certain lifestyle changes, such asregular exercise. This new treatment regimen could affect thousands ofpatients worldwide. Of the estimated 500,000 heart stent proceduresperformed annually in the United States, it is estimated that a fifth ofthose are for people with stable heart disease. It is further estimatedthat 25% of the estimated 100,000 people with stable heart disease, orroughly 23,000 people, are individuals that do not experience any chestpain. Accordingly, over 20,000 patients annually could potentially forgoinvasive surgical procedures or the complications resulting from suchprocedures.

To determine whether a patient should forego invasive surgicalprocedures and opt instead for a drug regimen and/or to generate a moreeffective treatment plan, it can be important to more fully understandthe cardiovascular disease of a patient. Specifically, it can beadvantageous to better understand the arterial vessel health of apatient. For example, it is helpful to understand whether plaquebuild-up in a patient is mostly fatty matter build-up or mostlycalcified matter build-up, because the former situation may warranttreatment with heart medicines, such as statins, whereas in the lattersituation a patient should be subject to further periodic monitoringwithout prescribing heart medicine or implanting any stents. However, ifthe plaque build-up is significant enough to cause severe stenosis ornarrowing of the arterial vessel such that blood flow to heart musclemight be blocked, then an invasive angioplasty procedure to implant astent may likely be required because heart attack or sudden cardiacdeath (SCD) could occur in such patients without the implantation of astent to enlarge the vessel opening. Sudden cardiac death is one of thelargest causes of natural death in the United States, accounting forapproximately 325,000 adult deaths per year and responsible for nearlyhalf of all deaths from cardiovascular disease. For males, SCD is twiceas common as compared to females. In general, SCD strikes people in themid-30 to mid-40 age range. In over 50% of cases, sudden cardiac arrestoccurs with no warning signs.

With respect to the millions suffering from heart disease, there is aneed to better understand the overall health of the artery vesselswithin a patient beyond just knowing the blood chemistry or content ofthe blood flowing through such artery vessels. For example, in someembodiments of systems, devices, and methods disclosed herein, arterieswith “good” or stable plaque or plaque comprising hardened calcifiedcontent are considered non-life threatening to patients whereas arteriescontaining “bad” or unstable plaque or plaque comprising fatty materialare considered more life threatening because such bad plaque may rupturewithin arteries thereby releasing such fatty material into the arteries.Such a fatty material release in the blood stream can cause inflammationthat may result in a blood clot. A blood clot within an artery canprevent blood from traveling to heart muscle thereby causing a heartattack or other cardiac event. Further, in some instances, it isgenerally more difficult for blood to flow through fatty plaque buildupthan it is for blood to flow through calcified plaque build-up.Therefore, there is a need for better understanding and analysis of thearterial vessel walls of a patient.

Further, while blood tests and drug treatment regimens are helpful inreducing cardiovascular health issues and mitigating againstcardiovascular events (for example, heart attacks), such treatmentmethodologies are not complete or perfect in that such treatments canmisidentify and/or fail to pinpoint or diagnose significantcardiovascular risk areas. For example, the mere analysis of the bloodchemistry of a patient will not likely identify that a patient hasartery vessels having significant amounts of fatty deposit material badplaque buildup along a vessel wall. Similarly, an angiogram, whilehelpful in identifying areas of stenosis or vessel narrowing, may not beable to clearly identify areas of the artery vessel wall where there issignificant buildup of bad plaque. Such areas of buildup of bad plaquewithin an artery vessel wall can be indicators of a patient at high riskof suffering a cardiovascular event, such as a heart attack. In certaincircumstances, areas where there exist areas of bad plaque can lead to arupture wherein there is a release of the fatty materials into thebloodstream of the artery, which in turn can cause a clot to develop inthe artery. A blood clot in the artery can cause a stoppage of bloodflow to the heart tissue, which can result in a heart attack.Accordingly, there is a need for new technology for analyzing arteryvessel walls and/or identifying areas within artery vessel walls thatcomprise a buildup of plaque whether it be bad or otherwise.

Various systems, methods, and devices disclosed herein are directed toembodiments for addressing the foregoing issues. In particular, variousembodiments described herein relate to systems, methods, and devices formedical image analysis, diagnosis, risk stratification, decision makingand/or disease tracking. In some embodiments, the systems, devices, andmethods described herein are configured to utilize non-invasive medicalimaging technologies, such as a CT image for example, which can beinputted into a computer system configured to automatically and/ordynamically analyze the medical image to identify one or more coronaryarteries and/or plaque within the same. For example, in someembodiments, the system can be configured to utilize one or more machinelearning and/or artificial intelligence algorithms to automaticallyand/or dynamically analyze a medical image to identify, quantify, and/orclassify one or more coronary arteries and/or plaque. In someembodiments, the system can be further configured to utilize theidentified, quantified, and/or classified one or more coronary arteriesand/or plaque to generate a treatment plan, track disease progression,and/or a patient-specific medical report, for example using one or moreartificial intelligence and/or machine learning algorithms. In someembodiments, the system can be further configured to dynamically and/orautomatically generate a visualization of the identified, quantified,and/or classified one or more coronary arteries and/or plaque, forexample in the form of a graphical user interface. Further, in someembodiments, to calibrate medical images obtained from different medicalimaging scanners and/or different scan parameters or environments, thesystem can be configured to utilize a normalization device comprisingone or more compartments of one or more materials.

As will be discussed in further detail, the systems, devices, andmethods described herein allow for automatic and/or dynamic quantifiedanalysis of various parameters relating to plaque, cardiovasculararteries, and/or other structures. More specifically, in someembodiments described herein, a medical image of a patient, such as acoronary CT image, can be taken at a medical facility. Rather thanhaving a physician eyeball or make a general assessment of the patient,the medical image is transmitted to a backend main server in someembodiments that is configured to conduct one or more analyses thereofin a reproducible manner. As such, in some embodiments, the systems,methods, and devices described herein can provide a quantifiedmeasurement of one or more features of a coronary CT image usingautomated and/or dynamic processes. For example, in some embodiments,the main server system can be configured to identify one or morevessels, plaque, and/or fat from a medical image. Based on theidentified features, in some embodiments, the system can be configuredto generate one or more quantified measurements from a raw medicalimage, such as for example radiodensity of one or more regions ofplaque, identification of stable plaque and/or unstable plaque, volumesthereof, surface areas thereof, geometric shapes, heterogeneity thereof,and/or the like. In some embodiments, the system can also generate oneor more quantified measurements of vessels from the raw medical image,such as for example diameter, volume, morphology, and/or the like. Basedon the identified features and/or quantified measurements, in someembodiments, the system can be configured to generate a risk assessmentand/or track the progression of a plaque-based disease or condition,such as for example atherosclerosis, stenosis, and/or ischemia, usingraw medical images. Further, in some embodiments, the system can beconfigured to generate a visualization of GUI of one or more identifiedfeatures and/or quantified measurements, such as a quantized colormapping of different features. In some embodiments, the systems,devices, and methods described herein are configured to utilize medicalimage-based processing to assess for a subject his or her risk of acardiovascular event, major adverse cardiovascular event (MACE), rapidplaque progression, and/or non-response to medication. In particular, insome embodiments, the system can be configured to automatically and/ordynamically assess such health risk of a subject by analyzing onlynon-invasively obtained medical images. In some embodiments, one or moreof the processes can be automated using an AI and/or ML algorithm. Insome embodiments, one or more of the processes described herein can beperformed within minutes in a reproducible manner. This is starkcontrast to existing measures today which do not produce reproducibleprognosis or assessment, take extensive amounts of time, and/or requireinvasive procedures.

As such, in some embodiments, the systems, devices, and methodsdescribed herein are able to provide physicians and/or patients specificquantified and/or measured data relating to a patient’s plaque that donot exist today. For example, in some embodiments, the system canprovide a specific numerical value for the volume of stable and/orunstable plaque, the ratio thereof against the total vessel volume,percentage of stenosis, and/or the like, using for example radiodensityvalues of pixels and/or regions within a medical image. In someembodiments, such detailed level of quantified plaque parameters fromimage processing and downstream analytical results can provide moreaccurate and useful tools for assessing the health and/or risk ofpatients in completely novel ways.

General Overview

In some embodiments, the systems, devices, and methods described hereinare configured to automatically and/or dynamically perform medical imageanalysis, diagnosis, risk stratification, decision making and/or diseasetracking. FIG. 1 is a flowchart illustrating an overview of an exampleembodiment(s) of a method for medical image analysis, visualization,risk assessment, disease tracking, treatment generation, and/or patientreport generation. As illustrated in FIG. 1 , in some embodiments, thesystem is configured to access and/or analyze one or more medical imagesof a subject, such as for example a medical image of a coronary regionof a subject or patient.

In some embodiments, before obtaining the medical image, a normalizationdevice is attached to the subject and/or is placed within a field ofview of a medical imaging scanner at block 102. For example, in someembodiments, the normalization device can comprise one or morecompartments comprising one or more materials, such as water, calcium,and/or the like. Additional detail regarding the normalization device isprovided below. Medical imaging scanners may produce images withdifferent scalable radiodensities for the same object. This, forexample, can depend not only on the type of medical imaging scanner orequipment used but also on the scan parameters and/or environment of theparticular day and/or time when the scan was taken. As a result, even iftwo different scans were taken of the same subject, the brightnessand/or darkness of the resulting medical image may be different, whichcan result in less than accurate analysis results processed from thatimage. To account for such differences, in some embodiments, anormalization device comprising one or more known elements is scannedtogether with the subject, and the resulting image of the one or moreknown elements can be used as a basis for translating, converting,and/or normalizing the resulting image. As such, in some embodiments, anormalization device is attached to the subject and/or placed within thefield of view of a medical imaging scan at a medical facility.

In some embodiments, at block 104, the medical facility then obtains oneor more medical images of the subject. For example, the medical imagecan be of the coronary region of the subject or patient. In someembodiments, the systems disclosed herein can be configured to take inCT data from the image domain or the projection domain as raw scanneddata or any other medical data, such as but not limited to: x-ray;Dual-Energy Computed Tomography (DECT), Spectral CT, photon-countingdetector CT, ultrasound, such as echocardiography or intravascularultrasound (IVUS); magnetic resonance (MR) imaging; optical coherencetomography (OCT); nuclear medicine imaging, including positron-emissiontomography (PET) and single photon emission computed tomography (SPECT);near-field infrared spectroscopy (NIRS); and/or the like. As usedherein, the term CT image data or CT scanned data can be substitutedwith any of the foregoing medical scanning modalities and process suchdata through an artificial intelligence (AI) algorithm system in orderto generate processed CT image data. In some embodiments, the data fromthese imaging modalities enables determination of cardiovascularphenotype, and can include the image domain data, the projection domaindata, and/or a combination of both.

In some embodiments, at block 106, the medical facility can also obtainnon-imaging data from the subject. For example, this can include bloodtests, biomarkers, panomics and/or the like. In some embodiments, atblock 108, the medical facility can transmit the one or more medicalimages and/or other non-imaging data at block 108 to a main serversystem. In some embodiments, the main server system can be configured toreceive and/or otherwise access the medical image and/or othernon-imaging data at block 110.

In some embodiments, at block 112, the system can be configured toautomatically and/or dynamically analyze the one or more medical imageswhich can be stored and/or accessed from a medical image database 100.For example, in some embodiments, the system can be configured to takein raw CT image data and apply an artificial intelligence (AI)algorithm, machine learning (ML) algorithm, and/or other physics-basedalgorithm to the raw CT data in order to identify, measure, and/oranalyze various aspects of the identified arteries within the CT data.In some embodiments, the inputting of the raw medical image datainvolves uploading the raw medical image data into cloud-based datarepository system. In some embodiments, the processing of the medicalimage data involves processing the data in a cloud-based computingsystem using an AI and/or ML algorithm. In some embodiments, the systemcan be configured to analyze the raw CT data in about 1 minute, about 2minutes, about 3 minutes, about 4 minutes, about 5 minutes, about 6minutes, about 7 minutes, about 8 minutes, about 9 minutes, about 10minutes, about 15 minutes, about 20 minutes, about 30 minutes, about 35minutes, about 40 minutes, about 45 minutes, about 50 minutes, about 55minutes, about 60 minutes, and/or within a range defined by two of theaforementioned values.

In some embodiments, the system can be configured to utilize a vesselidentification algorithm to identify and/or analyze one or more vesselswithin the medical image. In some embodiments, the system can beconfigured to utilize a coronary artery identification algorithm toidentify and/or analyze one or more coronary arteries within the medicalimage. In some embodiments, the system can be configured to utilize aplaque identification algorithm to identify and/or analyze one or moreregions of plaque within the medical image. In some embodiments, thevessel identification algorithm, coronary artery identificationalgorithm, and/or plaque identification algorithm comprises an AI and/orML algorithm. For example, in some embodiments, the vesselidentification algorithm, coronary artery identification algorithm,and/or plaque identification algorithm can be trained on a plurality ofmedical images wherein one or more vessels, coronary arteries, and/orregions of plaque are pre-identified. Based on such training, forexample by use of a Convolutional Neural Network in some embodiments,the system can be configured to automatically and/or dynamicallyidentify from raw medical images the presence and/or parameters ofvessels, coronary arteries, and/or plaque.

As such, in some embodiments, the processing of the medical image or rawCT scan data can comprise analysis of the medical image or CT data inorder to determine and/or identify the existence and/or nonexistence ofcertain artery vessels in a patient. As a natural occurring phenomenon,certain arteries may be present in certain patients whereas such certainarteries may not exist in other patients.

In some embodiments, at block 112, the system can be further configuredto analyze the identified vessels, coronary arteries, and/or plaque, forexample using an AI and/or ML algorithm. In particular, in someembodiments, the system can be configured to determine one or morevascular morphology parameters, such as for example arterial remodeling,curvature, volume, width, diameter, length, and/or the like. In someembodiments, the system can be configured to determine one or moreplaque parameters, such as for example volume, surface area, geometry,radiodensity, ratio or function of volume to surface area, heterogeneityindex, and/or the like of one or more regions of plaque shown within themedical image. “Radiodensity” as used herein is a broad term that refersto the relative inability of electromagnetic relation (e.g., X-rays) topass through a material. In reference to an image, radiodensity valuesrefer to values indicting a density in image data (e.g., film, print, orin an electronic format) where the radiodensity values in the imagecorresponds to the density of material depicted in the image.

In some embodiments, at block 114, the system can be configured toutilize the identified and/or analyzed vessels, coronary arteries,and/or plaque from the medical image to perform a point-in-time analysisof the subject. In some embodiments, the system can be configured to useautomatic and/or dynamic image processing of one or more medical imagestaken from one point in time to identify and/or analyze one or morevessels, coronary arteries, and/or plaque and derive one or moreparameters and/or classifications thereof. For example, as will bedescribed in more detail herein, in some embodiments, the system can beconfigured to generate one or more quantification metrics of plaqueand/or classify the identified regions of plaque as good or bad plaque.Further, in some embodiments, at block 114, the system can be configuredto generate one or more treatment plans for the subject based on theanalysis results. In some embodiments, the system can be configured toutilize one or more AI and/or ML algorithms to identify and/or analyzevessels or plaque, derive one or more quantification metrics and/orclassifications, and/or generate a treatment plan.

In some embodiments, if a previous scan or medical image of the subjectexists, the system can be configured to perform at block 126 one or moretime-based analyses, such as disease tracking. For example, in someembodiments, if the system has access to one or more quantifiedparameters or classifications derived from previous scans or medicalimages of the subject, the system can be configured to compare the samewith one or more quantified parameters or classifications derived from acurrent scan or medical image to determine the progression of diseaseand/or state of the subject.

In some embodiments, at block 116, the system is configured toautomatically and/or dynamically generate a Graphical User Interface(GUI) or other visualization of the analysis results at block 116, whichcan include for example identified vessels, regions of plaque, coronaryarteries, quantified metrics or parameters, risk assessment, proposedtreatment plan, and/or any other analysis result discussed herein. Insome embodiments, the system is configured to analyze arteries presentin the CT scan data and display various views of the arteries present inthe patient, for example within 10-15 minutes or less. In contrast, asan example, conducting a visual assessment of a CT to identify stenosisalone, without consideration of good or bad plaque or any other factor,can take anywhere between 15 minutes to more than an hour depending onthe skill level, and can also have substantial variability acrossradiologists and/or cardiac imagers.

In some embodiments, at block 118, the system can be configured totransmit the generated GUI or other visualization, analysis results,and/or treatment to the medical facility. In some embodiments, at block120, a physician at the medical facility can then review and/or confirmand/or revise the generated GUI or other visualization, analysisresults, and/or treatment.

In some embodiments, at block 122, the system can be configured tofurther generate and transmit a patient-specific medical report to apatient, who can receive the same at block 124. In some embodiments, thepatient-specific medical report can be dynamically generated based onthe analysis results derived from and/or other generated from themedical image processing and analytics. For example, thepatient-specific report can include identified vessels, regions ofplaque, coronary arteries, quantified metrics or parameters, riskassessment, proposed treatment plan, and/or any other analysis resultdiscussed herein.

In some embodiments, one or more of the process illustrated in FIG. 1can be repeated, for example for the same patient at a different time totrack progression of a disease and/or the state of the patient.

Image Processing-Based Classification of Good v. Bad Plaque

As discussed, in some embodiments, the systems, methods, and devicesdescribed herein are configured to automatically and/or dynamicallyidentify and/or classify good v. bad plaque or stable v. unstable plaquebased on medical image analysis and/or processing. For example, in someembodiments, the system can be configured to utilize an AI and/or MLalgorithm to identify areas in an artery that exhibit plaque buildupwithin, along, inside and/or outside the arteries. In some embodiments,the system can be configured to identify the outline or boundary ofplaque buildup associated with an artery vessel wall. In someembodiments, the system can be configured to draw or generate a linethat outlines the shape and configuration of the plaque buildupassociated with the artery. In some embodiments, the system can beconfigured to identify whether the plaque buildup is a certain kind ofplaque and/or the composition or characterization of a particular plaquebuildup. In some embodiments, the system can be configured tocharacterize plaque binarily, ordinally and/or continuously. In someembodiments, the system can be configured to determine that the kind ofplaque buildup identified is a “bad” kind of plaque due to the darkcolor or dark gray scale nature of the image corresponding to the plaquearea, and/or by determination of its attenuation density (e.g., using aHounsfield unit scale or other). For example, in some embodiments, thesystem can be configured to identify certain plaque as “bad” plaque ifthe brightness of the plaque is darker than a pre-determined level. Insome embodiments, the system can be configured to identify good plaqueareas based on the white coloration and/or the light gray scale natureof the area corresponding to the plaque buildup. For example, in someembodiments, the system can be configured to identify certain plaque as“good” plaque if the brightness of the plaque is lighter than apre-determined level. In some embodiments, the system can be configuredto determine that dark areas in the CT scan are related to “bad” plaque,whereas the system can be configured to identify good plaque areascorresponding to white areas. In some embodiments, the system can beconfigured to identify and determine the total area and/or volume oftotal plaque, good plaque, and/or bad plaque identified within an arteryvessel or plurality of vessels. In some embodiments, the system can beconfigured to determine the length of the total plaque area, good plaquearea, and/or bad plaque area identified. In some embodiments, the systemcan be configured to determine the width of the total plaque area, goodplaque area, and/or bad plaque area identified. The “good” plaque may beconsidered as such because it is less likely to cause heart attack, lesslikely to exhibit significant plaque progression, and/or less likely tobe ischemia, amongst others. Conversely, the “bad” plaque be consideredas such because it is more likely to cause heart attack, more likely toexhibit significant plaque progression, and/or more likely to beischemia, amongst others. In some embodiments, the “good” plaque may beconsidered as such because it is less likely to result in the no-reflowphenomenon at the time of coronary revascularization. Conversely, the“bad” plaque may be considered as such because it is more likely tocause the no-reflow phenomenon at the time of coronaryrevascularization.

FIG. 2A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for analysis and classification of plaque froma medical image, which can be obtained non-invasively. As illustrated inFIG. 2A, at block 202, in some embodiments, the system can be configuredto access a medical image, which can include a coronary region of asubject and/or be stored in a medical image database 100. The medicalimage database 100 can be locally accessible by the system and/or can belocated remotely and accessible through a network connection. Themedical image can comprise an image obtain using one or more modalitiessuch as for example, CT, Dual-Energy Computed Tomography (DECT),Spectral CT, photon-counting CT, x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), Magnetic Resonance (MR) imaging,optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), or near-field infrared spectroscopy (NIRS). In someembodiments, the medical image comprises one or more of acontrast-enhanced CT image, non-contrast CT image, MR image, and/or animage obtained using any of the modalities described above.

In some embodiments, the system can be configured to automaticallyand/or dynamically perform one or more analyses of the medical image asdiscussed herein. For example, in some embodiments, at block 204, thesystem can be configured to identify one or more arteries. The one ormore arteries can include coronary arteries, carotid arteries, aorta,renal artery, lower extremity artery, upper extremity artery, and/orcerebral artery, amongst others. In some embodiments, the system can beconfigured to utilize one or more AI and/or ML algorithms toautomatically and/or dynamically identify one or more arteries orcoronary arteries using image processing. For example, in someembodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of medical images onwhich arteries or coronary arteries have been identified, therebyallowing the AI and/or ML algorithm automatically identify arteries orcoronary arteries directly from a medical image. In some embodiments,the arteries or coronary arteries are identified by size and/orlocation.

In some embodiments, at block 206, the system can be configured toidentify one or more regions of plaque in the medical image. In someembodiments, the system can be configured to utilize one or more AIand/or ML algorithms to automatically and/or dynamically identify one ormore regions of plaque using image processing. For example, in someembodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of medical images onwhich regions of plaque have been identified, thereby allowing the AIand/or ML algorithm automatically identify regions of plaque directlyfrom a medical image. In some embodiments, the system can be configuredto identify a vessel wall and a lumen wall for each of the identifiedcoronary arteries in the medical image. In some embodiments, the systemis then configured to determine the volume in between the vessel walland the lumen wall as plaque. In some embodiments, the system can beconfigured to identify regions of plaque based on the radiodensityvalues typically associated with plaque, for example by setting apredetermined threshold or range of radiodensity values that aretypically associated with plaque with or without normalizing using anormalization device.

In some embodiments, the system is configured to automatically and/ordynamically determine one or more vascular morphology parameters and/orplaque parameters at block 208 from the medical image. In someembodiments, the one or more vascular morphology parameters and/orplaque parameters can comprise quantified parameters derived from themedical image. For example, in some embodiments, the system can beconfigured to utilize an AI and/or ML algorithm or other algorithm todetermine one or more vascular morphology parameters and/or plaqueparameters. As another example, in some embodiments, the system can beconfigured to determine one or more vascular morphology parameters, suchas classification of arterial remodeling due to plaque, which canfurther include positive arterial remodeling, negative arterialremodeling, and/or intermediate arterial remodeling. In someembodiments, the classification of arterial remodeling is determinedbased on a ratio of the largest vessel diameter at a region of plaque toa normal reference vessel diameter of the same region which can beretrieved from a normal database. In some embodiments, the system can beconfigured to classify arterial remodeling as positive when the ratio ofthe largest vessel diameter at a region of plaque to a normal referencevessel diameter of the same region is more than 1.1. In someembodiments, the system can be configured to classify arterialremodeling as negative when the ratio of the largest vessel diameter ata region of plaque to a normal reference vessel diameter is less than0.95. In some embodiments, the system can be configured to classifyarterial remodeling as intermediate when the ratio of the largest vesseldiameter at a region of plaque to a normal reference vessel diameter isbetween 0.95 and 1.1.

Further, as part of block 208, in some embodiments, the system can beconfigured to determine a geometry and/or volume of one or more regionsof plaque and/or one or more vessels or arteries at block 201. Forexample, the system can be configured to determine if the geometry of aparticular region of plaque is round or oblong or other shape. In someembodiments, the geometry of a region of plaque can be a factor inassessing the stability of the plaque. As another example, in someembodiments, the system can be configured to determine the curvature,diameter, length, volume, and/or any other parameters of a vessel orartery from the medical image.

In some embodiments, as part of block 208, the system can be configuredto determine a volume and/or surface area of a region of plaque and/or aratio or other function of volume to surface area of a region of plaqueat block 203, such as for example a diameter, radius, and/or thicknessof a region of plaque. In some embodiments, a plaque having a low ratioof volume to surface area can indicate that the plaque is stable. Assuch, in some embodiments, the system can be configured to determinethat a ratio of volume to surface area of a region of plaque below apredetermined threshold is indicative of stable plaque.

In some embodiments, as part of block 208, the system can be configuredto determine a heterogeneity index of a region of plaque at block 205.For instance, in some embodiments, a plaque having a low heterogeneityor high homogeneity can indicate that the plaque is stable. As such, insome embodiments, the system can be configured to determine that aheterogeneity of a region of plaque below a predetermined threshold isindicative of stable plaque. In some embodiments, heterogeneity orhomogeneity of a region of plaque can be determined based on theheterogeneity or homogeneity of radiodensity values within the region ofplaque. As such, in some embodiments, the system can be configured todetermine a heterogeneity index of plaque by generating spatial mapping,such as a three-dimensional histogram, of radiodensity values within oracross a geometric shape or region of plaque. In some embodiments, if agradient or change in radiodensity values across the spatial mapping isabove a certain threshold, the system can be configured to assign a highheterogeneity index. Conversely, in some embodiments, if a gradient orchange in radiodensity values across the spatial mapping is below acertain threshold, the system can be configured to assign a lowheterogeneity index.

In some embodiments, as part of block 208, the system can be configuredto determine a radiodensity of plaque and/or a composition thereof atblock 207. For example, a high radiodensity value can indicate that aplaque is highly calcified or stable, whereas a low radiodensity valuecan indicate that a plaque is less calcified or unstable. As such, insome embodiments, the system can be configured to determine that aradiodensity of a region of plaque above a predetermined threshold isindicative of stable stabilized plaque. In addition, different areaswithin a region of plaque can be calcified at different levels andthereby show different radiodensity values. As such, in someembodiments, the system can be configured to determine the radiodensityvalues of a region of plaque and/or a composition or percentage orchange of radiodensity values within a region of plaque. For instance,in some embodiments, the system can be configured to determine how muchor what percentage of plaque within a region of plaque shows aradiodensity value within a low range, medium range, high range, and/orany other classification.

Similarly, in some embodiments, as part of block 208, the system can beconfigured to determine a ratio of radiodensity value of plaque to avolume of plaque at block 209. For instance, it can be important toassess whether a large or small region of plaque is showing a high orlow radiodensity value. As such, in some embodiments, the system can beconfigured to determine a percentage composition of plaque comprisingdifferent radiodensity values as a function or ratio of volume ofplaque.

In some embodiments, as part of block 208, the system can be configuredto determine the diffusivity and/or assign a diffusivity index to aregion of plaque at block 211. For example, in some embodiments, thediffusivity of a plaque can depend on the radiodensity value of plaque,in which a high radiodensity value can indicate low diffusivity orstability of the plaque.

In some embodiments, at block 210, the system can be configured toclassify one or more regions of plaque identified from the medical imageas stable v. unstable or good v. bad based on the one or more vascularmorphology parameters and/or quantified plaque parameters determinedand/or derived from raw medical images. In particular, in someembodiments, the system can be configured to generate a weighted measureof one or more vascular morphology parameters and/or quantified plaqueparameters determined and/or derived from raw medical images. Forexample, in some embodiments, the system can be configured to weight oneor more vascular morphology parameters and/or quantified plaqueparameters equally. In some embodiments, the system can be configured toweight one or more vascular morphology parameters and/or quantifiedplaque parameters differently. In some embodiments, the system can beconfigured to weight one or more vascular morphology parameters and/orquantified plaque parameters logarithmically, algebraically, and/orutilizing another mathematical transform. In some embodiments, thesystem is configured to classify one or more regions of plaque at block210 using the generated weighted measure and/or using only some of thevascular morphology parameters and/or quantified plaque parameters.

In some embodiments, at block 212, the system is configured to generatea quantized color mapping based on the analyzed and/or determinedparameters. For example, in some embodiments, the system is configuredto generate a visualization of the analyzed medical image by generatinga quantized color mapping of calcified plaque, non-calcified plaque,good plaque, bad plaque, stable plaque, and/or unstable plaque asdetermined using any of the analytical techniques described herein.Further, in some embodiments, the quantified color mapping can alsoinclude arteries and/or epicardial fat, which can also be determined bythe system, for example by utilizing one or more AI and/or MLalgorithms.

In some embodiments, at block 214, the system is configured to generatea proposed treatment plan for the subject based on the analysis, such asfor example the classification of plaque derived automatically from araw medical image. In particular, in some embodiments, the system can beconfigured to assess or predict the risk of atherosclerosis, stenosis,and/or ischemia of the subject based on a raw medical image andautomated image processing thereof.

In some embodiments, one or more processes described herein inconnection with FIG. 2A can be repeated. For example, if a medical imageof the same subject is taken again at a later point in time, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for disease tracking and/or other purposes.

Determination of Non-Calcified Plaque From a Non-Contrast CT Image(s)

As discussed herein, in some embodiments, the system can be configuredto utilize a CT or other medical image of a subject as input forperforming one or more image analysis techniques to assess a subject,including for example risk of a cardiovascular event. In someembodiments, such CT image can comprise a contrast-enhanced CT image, inwhich case some of the analysis techniques described herein can bedirectly applied, for example to identify or classify plaque. However,in some embodiments, such CT image can comprise a non-contrast CT image,in which case it can be more difficult to identify and/or determinenon-calcified plaque due to its low radiodensity value and overlap withother low radiodensity values components, such as blood for example. Assuch, in some embodiments, the systems, devices, and methods describedherein provide a novel approach to determining non-calcified plaque froma non-contrast CT image, which can be more widely available.

Also, in some embodiments, in addition to or instead of analyzing acontrast-enhanced CT scan, the system can also be configured to examinethe attenuation densities within the arteries that are lower than theattenuation density of the blood flowing within them in a non-contrastCT scan. In some embodiments, these “low attenuation” plaques may bedifferentiated between the blood attenuation density and the fat thatsometimes surrounds the coronary artery and/or may representnon-calcified plaques of different materials. In some embodiments, thepresence of these non-calcified plaques may offer incremental predictionfor whether a previously calcified plaque is stabilizing or worsening orprogressing or regressing. These findings that are measurable throughthese embodiments may be linked to the prognosis of a patient, whereincalcium stabilization (that is, higher attenuation densities) and lackof non-calcified plaque by may associated with a favorable prognosis,while lack of calcium stabilization (that is, no increase in attenuationdensities), or significant progression or new calcium formation may beassociated with a poorer prognosis, including risk of rapid progressionof disease, heart attack or other major adverse cardiovascular event.

FIG. 2B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of non-calcified and/orlow-attenuated plaque from a medical image, such as a non-contrast CTimage. As discussed herein and as illustrated in FIG. 2B, in someembodiments, the system can be configured to determine non-calcifiedand/or low-attenuated plaque from a medical image. In some embodiments,the medical image can be of the coronary region of the subject orpatient. In some embodiments, the medical image can be obtained usingone or more modalities such as CT, Dual-Energy Computed Tomography(DECT), Spectral CT, x-ray, ultrasound, echocardiography, IVUS, MR, OCT,nuclear medicine imaging, PET, SPECT, NIRS, and/or the like. In someembodiments, the system can be configured to access one or more medicalimages at block 202, for example from a medical image database 100.

In some embodiments, in order to determine non-calcified and/orlow-attenuated plaque from the medical image or non-contrast CT image,the system can be configured to utilize a stepwise approach to firstidentify areas within the medical image that are clearly non-calcifiedplaque. In some embodiments, the system can then conduct a more detailedanalysis of the remaining areas in the image to identify other regionsof non-calcified and/or low-attenuated plaque. By utilizing suchcompartmentalized or a stepwise approach, in some embodiments, thesystem can identify or determine non-calcified and/or low-attenuatedplaque from the medical image or non-contrast CT image with a fasterturnaround rather than having to apply a more complicated analysis toevery region or pixel of the image.

In particular, in some embodiments, at block 224, the system can beconfigured to identify epicardial fat from the medical image. In someembodiments, the system can be configured to identify epicardial fat bydetermining every pixel or region within the image that has aradiodensity value below a predetermined threshold and/or within apredetermined range. The exact predetermined threshold value or range ofradiodensity for identifying epicardial fat can depend on the medicalimage, scanner type, scan parameters, and/or the like, which is why anormalization device can be used in some instances to normalize themedical image. For example, in some embodiments, the system can beconfigured to identify as epicardial fat pixels and/or regions withinthe medical image or non-contrast CT image with a radiodensity valuethat is around -100 Hounsfield units and/or within a range that includes-100 Hounsfield units. In particular, in some embodiments, the systemcan be configured to identify as epicardial fat pixels and/or regionswithin the medical image or non-contrast CT image with a radiodensityvalue that is within a range with a lower limit of about -100 Hounsfieldunits, about -110 Hounsfield units, about -120 Hounsfield units, about-130 Hounsfield units, about -140 Hounsfield units, about -150Hounsfield units, about -160 Hounsfield units, about -170 Hounsfieldunits, about -180 Hounsfield units, about -190 Hounsfield units, orabout -200 Hounsfield units, and an upper limit of about 30 Hounsfieldunits, about 20 Hounsfield units, about 10 Hounsfield units, about 0Hounsfield units, about -10 Hounsfield units, about -20 Hounsfieldunits, about -30 Hounsfield units, about -40 Hounsfield units, about -50Hounsfield units, about -60 Hounsfield units, about -70 Hounsfieldunits, about -80 Hounsfield units, or about -90 Hounsfield units.

In some embodiments, the system can be configured to identify and/orsegment arteries on the medical image or non-contrast CT image using theidentified epicardial fat as outer boundaries of the arteries. Forexample, the system can be configured to first identify regions ofepicardial fat on the medical image and assign a volume in betweenepicardial fat as an artery, such as a coronary artery.

In some embodiments, at block 226, the system can be configured toidentify a first set of pixels or regions within the medical image, suchas within the identified arteries, as non-calcified or low-attenuatedplaque. More specifically, in some embodiments, the system can beconfigured to identify as an initial set low-attenuated or non-calcifiedplaque by identifying pixels or regions with a radiodensity value thatis below a predetermined threshold or within a predetermined range. Forexample, the predetermined threshold or predetermined range can be setsuch that the resulting pixels can be confidently marked aslow-attenuated or non-calcified plaque without likelihood of confusionwith another matter such as blood. In particular, in some embodiments,the system can be configured to identify the initial set oflow-attenuated or non-calcified plaque by identifying pixels or regionswith a radiodensity value below around 30 Hounsfield units. In someembodiments, the system can be configured to identify the initial set oflow-attenuated or non-calcified plaque by identifying pixels or regionswith a radiodensity value at or below around 60 Hounsfield units, around55 Hounsfield units, around 50 Hounsfield units, around 45 Hounsfieldunits, around 40 Hounsfield units, around 35 Hounsfield units, around 30Hounsfield units, around 25 Hounsfield units, around 20 Hounsfieldunits, around 15 Hounsfield units, around 10 Hounsfield units, around 5Hounsfield units, and/or with a radiodensity value at or above around 0Hounsfield units, around 5 Hounsfield units, around 10 Hounsfield units,around 15 Hounsfield units, around 20 Hounsfield units, around 25Hounsfield units, and/or around 30 Hounsfield units. In someembodiments, the system can be configured classify pixels or regionsthat fall within or below this predetermined range of radiodensityvalues as a first set of identified non-calcified or low-attenuatedplaque at block 238.

In some embodiments, the system at block 228 can be configured toidentify a second set of pixels or regions within the medical image,such as within the identified arteries, that may or may not representlow-attenuated or non-calcified plaque. As discussed, in someembodiments, this second set of candidates of pixels or regions mayrequire additional analysis to confirm that they represent plaque. Inparticular, in some embodiments, the system can be configured toidentify this second set of pixels or regions that may potentially below-attenuated or non-calcified plaque by identifying pixels or regionsof the image with a radiodensity value within a predetermined range. Insome embodiments, the predetermined range for identifying this secondset of pixels or regions can be between around 30 Hounsfield units and100 Hounsfield units. In some embodiments, the predetermined range foridentifying this second set of pixels or regions can have a lower limitof around 0 Hounsfield units, 5 Hounsfield units, 10 Hounsfield units,15 Hounsfield units, 20 Hounsfield units, 25 Hounsfield units, 30Hounsfield units, 35 Hounsfield units, 40 Hounsfield units, 45Hounsfield units, 50 Hounsfield units, and/or an upper limit of around55 Hounsfield units, 60 Hounsfield units, 65 Hounsfield units, 70Hounsfield units, 75 Hounsfield units, 80 Hounsfield units, 85Hounsfield units, 90 Hounsfield units, 95 Hounsfield units, 100Hounsfield units, 110 Hounsfield units, 120 Hounsfield units, 130Hounsfield units, 140 Hounsfield units, 150 Hounsfield units.

In some embodiments, at block 230, the system can be configured conductan analysis of the heterogeneity of the identified second set of pixelsor regions. For example, depending on the range of radiodensity valuesused to identify the second set of pixels, in some embodiments, thesecond set of pixels or regions may include blood and/or plaque. Bloodcan typically show a more homogeneous gradient of radiodensity valuescompared to plaque. As such, in some embodiments, by analyzing thehomogeneity or heterogeneity of the pixels or regions identified as partof the second set, the system can be able to distinguish between bloodand non-calcified or low attenuated plaque. As such, in someembodiments, the system can be configured to determine a heterogeneityindex of the second set of regions of pixels identified from the medicalimage by generating spatial mapping, such as a three-dimensionalhistogram, of radiodensity values within or across a geometric shape orregion of plaque. In some embodiments, if a gradient or change inradiodensity values across the spatial mapping is above a certainthreshold, the system can be configured to assign a high heterogeneityindex and/or classify as plaque. Conversely, in some embodiments, if agradient or change in radiodensity values across the spatial mapping isbelow a certain threshold, the system can be configured to assign a lowheterogeneity index and/or classify as blood.

In some embodiments, at block 240, the system can be configured toidentify a subset of the second set of regions of pixels identified fromthe medical image as plaque or non-calcified or low-attenuated plaque.In some embodiments, at block 242, the system can be configured tocombine the first set of identified non-calcified or low-attenuatedplaque from block 238 and the second set of identified non-calcified orlow-attenuated plaque from block 240. As such, even using non-contrastCT images, in some embodiments, the system can be configured to identifylow-attenuated or non-calcified plaque which can be more difficult toidentify compared to calcified or high-attenuated plaque due to possibleoverlap with other matter such as blood.

In some embodiments, the system can also be configured to determinecalcified or high-attenuated plaque from the medical image at block 232.This process can be more straightforward compared to identifyinglow-attenuated or non-calcified plaque from the medical image ornon-contrast CT image. In particular, in some embodiments, the systemcan be configured to identify calcified or high-attenuated plaque fromthe medical image or non-contrast CT image by identifying pixels orregions within the image that have a radiodensity value above apredetermined threshold and/or within a predetermined range. Forexample, in some embodiments, the system can be configured to identifyas calcified or high-attenuated plaque regions or pixels from themedical image or non-contrast CT image having a radiodensity value abovearound 100 Hounsfield units, around 150 Hounsfield units, around 200Hounsfield units, around 250 Hounsfield units, around 300 Hounsfieldunits, around 350 Hounsfield units, around 400 Hounsfield units, around450 Hounsfield units, around 500 Hounsfield units, around 600 Hounsfieldunits, around 700 Hounsfield units, around 800 Hounsfield units, around900 Hounsfield units, around 1000 Hounsfield units, around 1100Hounsfield units, around 1200 Hounsfield units, around 1300 Hounsfieldunits, around 1400 Hounsfield units, around 1500 Hounsfield units,around 1600 Hounsfield units, around 1700 Hounsfield units, around 1800Hounsfield units, around 1900 Hounsfield units, around 2000 Hounsfieldunits, around 2500 Hounsfield units, around 3000 Hounsfield units,and/or any other minimum threshold.

In some embodiments, at block 234, the system can be configured togenerate a quantized color mapping of one or more identified mattersfrom the medical image. For example, in some embodiments, the system canbe configured assign different colors to each of the different regionsassociated with different matters, such as non-calcified orlow-attenuated plaque, calcified or high-attenuated plaque, all plaque,arteries, epicardial fat, and/or the like. In some embodiments, thesystem can be configured to generate a visualization of the quantizedcolor map and/or present the same to a medical personnel or patient viaa GUI. In some embodiments, at block 236, the system can be configuredto generate a proposed treatment plan for a disease based on one or moreof the identified non-calcified or low-attenuated plaque, calcified orhigh-attenuated plaque, all plaque, arteries, epicardial fat, and/or thelike. For example, in some embodiments, the system can be configured togenerate a treatment plan for an arterial disease, renal artery disease,abdominal atherosclerosis, carotid atherosclerosis, and/or the like, andthe medical image being analyzed can be taken from any one or moreregions of the subject for such disease analysis.

In some embodiments, one or more processes described herein inconnection with FIG. 2B can be repeated. For example, if a medical imageof the same subject is taken again at a later point in time, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for disease tracking and/or other purposes.

Further, in some embodiments, the system can be configured to identifyand/or determine non-calcified plaque from a DECT or spectral CT image.Similar to the processes described above, in some embodiments, thesystem can be configured to access a DECT or spectral CT image, identifyepicardial fat on the DECT image or spectral CT and/or segment one ormore arteries on the DECT image or spectral CT, identify and/or classifya first set of pixels or regions within the arteries as a first set oflow-attenuated or non-calcified plaque, and/or identify a second set ofpixels or regions within the arteries as a second set of low-attenuatedor non-calcified plaque. However, unlike the techniques described above,in some embodiments, such as for example where a DECT or spectral CTimage is being analyzed, the system can be configured to identify asubset of those second set of pixels without having to perform aheterogeneity and/or homogeneity analysis of the second set of pixels.Rather, in some embodiments, the system can be configured to distinguishbetween blood and low-attenuated or non-calcified plaque directly fromthe image, for example by utilizing the dual or multispectral aspect ofa DECT or spectral CT image. In some embodiments, the system can beconfigured to combine the first set of identified pixels or regions andthe subset of the second set of pixels or regions identified aslow-attenuated or non-calcified plaque to identify a whole set of thesame on the medical image. In some embodiments, even if analyzing a DECTor spectral CT image, the system can be configured to further analyzethe second set of pixels or regions by performing a heterogeneity orhomogeneity analysis, similar to that described above in relation toblock 230. For example, even if analyzing a DECT or spectral CT image,in some embodiments, the distinction between certain areas of bloodand/or low-attenuated or non-calcified plaque may not be complete and/oraccurate.

Imaging Analysis-Based Risk Assessment

In some embodiments, the systems, devices, and methods described hereinare configured to utilize medical image-based processing to assess for asubject his or her risk of a cardiovascular event, major adversecardiovascular event (MACE), rapid plaque progression, and/ornon-response to medication. In particular, in some embodiments, thesystem can be configured to automatically and/or dynamically assess suchhealth risk of a subject by analyzing only non-invasively obtainedmedical images, for example using AI and/or ML algorithms, to provide afull image-based analysis report within minutes.

In particular, in some embodiments, the system can be configured tocalculate the total amount of plaque (and/or amounts of specific typesof plaque) within a specific artery and/or within all the arteries of apatient. In some embodiments, the system can be configured to determinethe total amount of bad plaque in a particular artery and/or within atotal artery area across some or all of the arteries of the patient. Insome embodiments, the system can be configured to determine a riskfactor and/or a diagnosis for a particular patient to suffer a heartattack or other cardiac event based on the total amount of plaque in aparticular artery and/or a total artery area across some or all of thearteries of a patient. Other risk factors that can be determined fromthe amount of “bad” plaque, or the relative amount of “bad” versus“good” plaque, can include the rate of disease progression and/or thelikelihood of ischemia. In some embodiments, plaques can be measured bytotal volume (or area on cross-sectional imaging) as well as by relativeamount when normalized to the total vessel volumes, total vessel lengthsor subtended myocardium.

In some embodiments, the imaging data of the coronary arteries caninclude measures of atherosclerosis, stenosis and vascular morphology.In some embodiments, this information can be combined with othercardiovascular disease phenotyping by quantitative characterization ofleft and right ventricles, left and right atria; aortic, mitral,tricuspid and pulmonic valves; aorta, pulmonary artery, pulmonary vein,coronary sinus and inferior and superior vena cava; epicardial orpericoronary fat; lung densities; bone densities; pericardium andothers. As an example, in some embodiments, the imaging data for thecoronary arteries may be integrated with the left ventricular mass,which can be segmented according to the amount and location of theartery it is subtended by. This combination of left ventricularfractional myocardial mass to coronary artery information may enhancethe prediction of whether a future heart attack will be a large one or asmall one. As another example, in some embodiments, the vessel volume ofthe coronary arteries can be related to the left ventricular mass as ameasure of left ventricular hypertrophy, which can be a common findingin patients with hypertension. Increased left ventricular mass (relativeor absolute) may indicate disease worsening or uncontrolledhypertension. As another example, in some embodiments, the onset,progression, and/or worsening of atrial fibrillation may be predicted bythe atrial size, volume, atrial free wall mass and thickness, atrialfunction and fat surrounding the atrium. In some embodiments, thesepredictions may be done with a ML or AI algorithm or other algorithmtype.

Sequentially, in some embodiments, the algorithms that allow forsegmentation of atherosclerosis, stenosis and vascular morphology—alongwith those that allow for segmentation of other cardiovascularstructures, and thoracic structures—may serve as the inputs for theprognostic algorithms. In some embodiments, the outputs of theprognostic algorithms, or those that allow for image segmentation, maybe leveraged as inputs to other algorithms that may then guide clinicaldecision making by predicting future events. As an example, in someembodiments, the integrated scoring of atherosclerosis, stenosis, and/orvascular morphology may identify patients who may benefit from coronaryrevascularization, that is, those who will achieve symptom benefit,reduced risk of heart attack and death. As another example, in someembodiments, the integrated scoring of atherosclerosis, stenosis andvascular morphology may identify individuals who may benefit fromspecific types of medications, such as lipid lowering medications (suchas statin medications, PCSK-9 inhibitors, icosapent ethyl, and others);Lp(a) lowering medications; anti-thrombotic medications (such asclopidogrel, rivaroxaban and others). In some embodiments, the benefitthat is predicted by these algorithms may be for reduced progression,determination of type of plaque progression (progression, regression ormixed response), stabilization due to the medical therapy, and/or needfor heightened intensified therapy. In some embodiments, the imagingdata may be combined with other data to identify areas within a coronaryvessel that are normal and without plaque now but may be at higherlikelihood of future plaque formation.

In some embodiments, an automated or manual co-registration method canbe combined with the imaging segmentation data to compare two or moreimages over time. In some embodiments, the comparison of these imagescan allow for determination of differences in coronary arteryatherosclerosis, stenosis and vascular morphology over time, and can beused as an input variable for risk prediction.

In some embodiments, the imaging data of the coronary arteries foratherosclerosis, stenosis, and vascular morphology-coupled or notcoupled to thoracic and cardiovascular disease measurements—can beintegrated into an algorithm that determines whether a coronary vesselis ischemia, or exhibits reduced blood flow or pressure (either at restor hyperemic states).

In some embodiments, the algorithms for coronary atherosclerosis,stenosis and ischemia can be modified by a computer system and/or otherto remove plaque or “seal” plaque. In some embodiments, a comparison canbe made before or after the system has removed or sealed the plaque todetermine whether any changes have occurred. For example, in someembodiments, the system can be configured to determine whether coronaryischemia is removed with the plaque sealing.

In some embodiments, the characterization of coronary atherosclerosis,stenosis and/or vascular morphology can enable relating a patient’sbiological age to their vascular age, when compared to apopulation-based cohort of patients who have undergone similar scanning.As an example, a 60-year old patient may have X units of plaque in theircoronary arteries that is equivalent to the average 70-year old patientin the population-based cohort. In this case, the patient’s vascular agemay be 10 years older than the patient’s biological age.

In some embodiments, the risk assessment enabled by the imagesegmentation prediction algorithms can allow for refined measures ofdisease or death likelihood in people being considered for disability orlife insurance. In this scenario, the risk assessment may replace oraugment traditional actuarial algorithms.

In some embodiments, imaging data may be combined with other data toaugment risk assessment for future adverse events, such as heartattacks, strokes, death, rapid progression, non-response to medicaltherapy, no-reflow phenomenon and others. In some embodiments, otherdata may include a multi-omic approach wherein an algorithm integratesthe imaging phenotype data with genotype data, proteomic data,transcriptomic data, metabolomic data, microbiomic data and/or activityand lifestyle data as measured by a smart phone or similar device.

FIG. 3A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for risk assessment based on medical imageanalysis. As illustrated in FIG. 3A, in some embodiments, the system canbe configured to access a medical image at block 202. Further, in someembodiments, the system can be configured to identify one or morearteries at block 204 and/or one or more regions of plaque at block 206.In addition, in some embodiments, the system can be configured todetermine one or more vascular morphology and/or quantified plaqueparameters at block 208 and/or classify stable or unstable plaque basedon the determined one or more vascular morphology and/or quantifiedplaque parameters and/or a weighted measure thereof at block 210.Additional detail regarding the processes and techniques represented inblocks 202, 204, 206, 208, and 210 can be found in the description abovein relation to FIG. 2A.

In some embodiments, the system can automatically and/or dynamicallydetermine and/or generate a risk of cardiovascular event for the subjectat block 302, for example using the classified stable and/or unstableregions of plaque. More specifically, in some embodiments, the systemcan utilize an AI, ML, or other algorithm to generate a risk ofcardiovascular event, MACE, rapid plaque progression, and/ornon-response to medication at block 302 based on the image analysis.

In some embodiments, at block 304, the system can be configured tocompare the determined one or more vascular morphology parameters,quantified plaque parameters, and/or classified stable v. unstableplaque and/or values thereof, such as volume, ratio, and/or the like, toone or more known datasets of coronary values derived from one or moreother subjects. The one or more known datasets can comprise one or morevascular morphology parameters, quantified plaque parameters, and/orclassified stable v. unstable plaque and/or values thereof, such asvolume, ratio, and/or the like, derived from medical images taken fromother subjects, including healthy subjects and/or subjects with varyinglevels of risk. For example, the one or more known datasets of coronaryvalues can be stored in a coronary values database 306 that can belocally accessible by the system and/or remotely accessible via anetwork connection by the system.

In some embodiments, at block 308, the system can be configured toupdate the risk of cardiovascular event for the subject based on thecomparison to the one or more known datasets. For example, based on thecomparison, the system may increase or decrease the previously generatedrisk assessment. In some embodiments, the system may maintain thepreviously generated risk assessment even after comparison. In someembodiments, the system can be configured to generate a proposedtreatment for the subject based on the generated and/or updated riskassessment after comparison with the known datasets of coronary values.

In some embodiments, at block 310, the system can be configured tofurther identify one or more other cardiovascular structures from themedical image and/or determine one or more parameters associated withthe same. For example, the one or more additional cardiovascularstructures can include the left ventricle, right ventricle, left atrium,right atrium, aortic valve, mitral valve, tricuspid valve, pulmonicvalve, aorta, pulmonary artery, inferior and superior vena cava,epicardial fat, and/or pericardium.

In some embodiments, parameters associated with the left ventricle caninclude size, mass, volume, shape, eccentricity, surface area,thickness, and/or the like. Similarly, in some embodiments, parametersassociated with the right ventricle can include size, mass, volume,shape, eccentricity, surface area, thickness, and/or the like. In someembodiments, parameters associated with the left atrium can includesize, mass, volume, shape, eccentricity, surface area, thickness,pulmonary vein angulation, atrial appendage morphology, and/or the like.In some embodiments, parameters associated with the right atrium caninclude size, mass, volume, shape, eccentricity, surface area,thickness, and/or the like.

Further, in some embodiments, parameters associated with the aorticvalve can include thickness, volume, mass, calcifications,three-dimensional map of calcifications and density, eccentricity ofcalcification, classification by individual leaflet, and/or the like. Insome embodiments, parameters associated with the mitral valve caninclude thickness, volume, mass, calcifications, three-dimensional mapof calcifications and density, eccentricity of calcification,classification by individual leaflet, and/or the like. In someembodiments, parameters associated with the tricuspid valve can includethickness, volume, mass, calcifications, three-dimensional map ofcalcifications and density, eccentricity of calcification,classification by individual leaflet, and/or the like. In someembodiments, parameters associated with the pulmonic valve can includethickness, volume, mass, calcifications, three-dimensional map ofcalcifications and density, eccentricity of calcification,classification by individual leaflet, and/or the like.

In some embodiments, parameters associated with the aorta can includedimensions, volume, diameter, area, enlargement, outpouching, and/or thelike. In some embodiments, parameters associated with the pulmonaryartery can include dimensions, volume, diameter, area, enlargement,outpouching, and/or the like. In some embodiments, parameters associatedwith the inferior and superior vena cava can include dimensions, volume,diameter, area, enlargement, outpouching, and/or the like.

In some embodiments, parameters associated with epicardial fat caninclude volume, density, density in three dimensions, and/or the like.In some embodiments, parameters associated with the pericardium caninclude thickness, mass, and/or the like.

In some embodiments, at block 312, the system can be configured toclassify one or more of the other identified cardiovascular structures,for example using the one or more determined parameters thereof. In someembodiments, for one or more of the other identified cardiovascularstructures, the system can be configured to classify each as normal v.abnormal, increased or decreased, and/or static or dynamic over time.

In some embodiments, at block 314, the system can be configured tocompare the determined one or more parameters of other cardiovascularstructures to one or more known datasets of cardiovascular structureparameters derived from one or more other subjects. The one or moreknown datasets of cardiovascular structure parameters can include anyone or more of the parameters mentioned above associated with the othercardiovascular structures. In some embodiments, the cardiovascularstructure parameters of the one or more known datasets can be derivedfrom medical images taken from other subjects, including healthysubjects and/or subjects with varying levels of risk. In someembodiments, the one or more known datasets of cardiovascular structureparameters can be stored in a cardiovascular structure values orcardiovascular disease (CVD) database 316 that can be locally accessibleby the system and/or remotely accessible via a network connection by thesystem.

In some embodiments, at block 318, the system can be configured toupdate the risk of cardiovascular event for the subject based on thecomparison to the one or more known datasets of cardiovascular structureparameters. For example, based on the comparison, the system mayincrease or decrease the previously generated risk assessment. In someembodiments, the system may maintain the previously generated riskassessment even after comparison.

In some embodiments, at block 320, the system can be configured togenerate a quantified color map, which can include color coding for oneor more other cardiovascular structures identified from the medicalimage, stable plaque, unstable plaque, arteries, and/or the like. Insome embodiments, at block 322, the system can be configured to generatea proposed treatment for the subject based on the generated and/orupdated risk assessment after comparison with the known datasets ofcardiovascular structure parameters.

In some embodiments, at block 324, the system can be configured tofurther identify one or more non-cardiovascular structures from themedical image and/or determine one or more parameters associated withthe same. For example, the medical image can include one or morenon-cardiovascular structures that are in the field of view. Inparticular, the one or more non-cardiovascular structures can includethe lungs, bones, liver, and/or the like.

In some embodiments, parameters associated with the non-cardiovascularstructures can include volume, surface area, ratio or function of volumeto surface area, heterogeneity of radiodensity values, radiodensityvalues, geometry (such as oblong, spherical, and/or the like), spatialradiodensity, spatial scarring, and/or the like. In addition, in someembodiments, parameters associated with the lungs can include density,scarring, and/or the like. For example, in some embodiments, the systemcan be configured to associate a low Hounsfield unit of a region of thelungs with emphysema. In some embodiments, parameters associated withbones, such as the spine and/or ribs, can include radiodensity, presenceand/or extent of fractures, and/or the like. For example, in someembodiments, the system can be configured to associate a low Hounsfieldunit of a region of bones with osteoporosis. In some embodiments,parameters associated with the liver can include density fornon-alcoholic fatty liver disease which can be assessed by the system byanalyzing and/or comparing to the Hounsfield unit density of the liver.

In some embodiments, at block 326, the system can be configured toclassify one or more of the identified non-cardiovascular structures,for example using the one or more determined parameters thereof. In someembodiments, for one or more of the identified non-cardiovascularstructures, the system can be configured to classify each as normal v.abnormal, increased or decreased, and/or static or dynamic over time.

In some embodiments, at block 328, the system can be configured tocompare the determined one or more parameters of non-cardiovascularstructures to one or more known datasets of non-cardiovascular structureparameters or non-CVD values derived from one or more other subjects.The one or more known datasets of non-cardiovascular structureparameters or non-CVD values can include any one or more of theparameters mentioned above associated with non-cardiovascularstructures. In some embodiments, the non-cardiovascular structureparameters or non-CVD values of the one or more known datasets can bederived from medical images taken from other subjects, including healthysubjects and/or subjects with varying levels of risk. In someembodiments, the one or more known datasets of non-cardiovascularstructure parameters or non-CVD values can be stored in anon-cardiovascular structure values or non-CVD database 330 that can belocally accessible by the system and/or remotely accessible via anetwork connection by the system.

In some embodiments, at block 332, the system can be configured toupdate the risk of cardiovascular event for the subject based on thecomparison to the one or more known datasets of non-cardiovascularstructure parameters or non-CVD values. For example, based on thecomparison, the system may increase or decrease the previously generatedrisk assessment. In some embodiments, the system may maintain thepreviously generated risk assessment even after comparison.

In some embodiments, at block 334, the system can be configured togenerate a quantified color map, which can include color coding for oneor more non-cardiovascular structures identified from the medical image,as well as for the other cardiovascular structures identified from themedical image, stable plaque, unstable plaque, arteries, and/or thelike. In some embodiments, at block 336, the system can be configured togenerate a proposed treatment for the subject based on the generatedand/or updated risk assessment after comparison with the known datasetsof non-cardiovascular structure parameters or non-CVD values.

In some embodiments, one or more processes described herein inconnection with FIG. 3A can be repeated. For example, if a medical imageof the same subject is taken again at a later point in time, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for tracking of risk assessment of the subject basedon image processing and/or other purposes.

Quantification of Atherosclerosis

In some embodiments, the system is configured to analyze one or morearteries present in a medical image, such as CT scan data, toautomatically and/or dynamically quantify atherosclerosis. In someembodiments, the system is configured to quantify atherosclerosis as theprimary disease process, while stenosis and/or ischemia can beconsidered surrogates thereof. Prior to the embodiments describedherein, it was not feasible to quantify the primary disease due to thelengthy manual process and manpower needed to do so, which could takeanywhere from 4 to 8 or more hours. In contrast, in some embodiments,the system is configured to quantify atherosclerosis based on analysisof a medical image and/or CT scan using one or more AI, ML, and/or otheralgorithms that can segment, identify, and/or quantify atherosclerosisin less than about 1 minute, about 2 minutes, about 3 minutes, about 4minutes, about 5 minutes, about 6 minutes, about 7 minutes, about 8minutes, about 9 minutes, about 10 minutes, about 11 minutes, about 12minutes, about 13 minutes, about 14 minutes, about 15 minutes, about 20minutes, about 25 minutes, about 30 minutes, about 40 minutes, about 50minutes, and/or about 60 minutes. In some embodiments, the system isconfigured to quantify atherosclerosis within a time frame defined bytwo of the aforementioned values. In some embodiments, the system isconfigured to calculate stenosis rather than simply eyeballing, therebyallowing users to better understand whole heart atherosclerosis and/orguaranteeing the same calculated stenosis result if the same medicalimage is used for analysis. Importantly, the type of atherosclerosis canalso be quantified and/or classified by this method. Types ofatherosclerosis can be determined binarily (calcified vs. non-calcifiedplaque), ordinally (dense calcified plaque, calcified plaque, fibrousplaque, fibrofatty plaque, necrotic core, or admixtures of plaquetypes), or continuously (by attenuation density on a Hounsfield unitscale or similar).

FIG. 3B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for quantification and/or classification ofatherosclerosis based on medical image analysis. As illustrated in FIG.3B, in some embodiments, the system can be configured to access amedical image at block 202, such as a CT scan of a coronary region of asubject. Further, in some embodiments, the system can be configured toidentify one or more arteries at block 204 and/or one or more regions ofplaque at block 206. In addition, in some embodiments, the system can beconfigured to determine one or more vascular morphology and/orquantified plaque parameters at block 208. For example, in someembodiments, the system can be configured to determine a geometry and/orvolume of a region of plaque and/or a vessel at block 201, a ratio orfunction of volume to surface area of a region of plaque at block 203, aheterogeneity or homogeneity index of a region of plaque at block 205,radiodensity of a region of plaque and/or a composition thereof byranges of radiodensity values at block 207, a ratio of radiodensity tovolume of a region of plaque at block 209, and/or a diffusivity of aregion of plaque at block 211. Additional detail regarding the processesand techniques represented in blocks 202, 204, 206, 208, 201, 203, 205,207, 209, and 211 can be found in the description above in relation toFIG. 2A.

In some embodiments, the system can be configured quantify and/orclassify atherosclerosis at block 340 based on the determined one ormore vascular morphology and/or quantified plaque parameters. In someembodiments, the system can be configured to generate a weighted measureof one or more vascular morphology parameters and/or quantified plaqueparameters determined and/or derived from raw medical images. Forexample, in some embodiments, the system can be configured to weight oneor more vascular morphology parameters and/or quantified plaqueparameters equally. In some embodiments, the system can be configuredweight one or more vascular morphology parameters and/or quantifiedplaque parameters differently. In some embodiments, the system can beconfigured weight one or more vascular morphology parameters and/orquantified plaque parameters logarithmically, algebraically, and/orutilizing another mathematical transform. In some embodiments, thesystem is configured to quantify and/or classify atherosclerosis atblock 340 using the weighted measure and/or using only some of thevascular morphology parameters and/or quantified plaque parameters.

In some embodiments, the system is configured to generate a weightedmeasure of the one or more vascular morphology parameters and/orquantified plaque parameters by comparing the same to one or more knownvascular morphology parameters and/or quantified plaque parameters thatare derived from medical images of other subjects. For example, the oneor more known vascular morphology parameters and/or quantified plaqueparameters can be derived from one or more healthy subjects and/orsubjects at risk of coronary vascular disease.

In some embodiments, the system is configured to classifyatherosclerosis of a subject based on the quantified atherosclerosis asone or more of high risk, medium risk, or low risk. In some embodiments,the system is configured to classify atherosclerosis of a subject basedon the quantified atherosclerosis using an AI, ML, and/or otheralgorithm. In some embodiments, the system is configured to classifyatherosclerosis of a subject by combining and/or weighting one or moreof a ratio of volume of surface area, volume, heterogeneity index, andradiodensity of the one or more regions of plaque.

In some embodiments, a plaque having a low ratio of volume to surfacearea or a low absolute volume itself can indicate that the plaque isstable. As such, in some embodiments, the system can be configured todetermine that a ratio of volume to surface area of a region of plaquebelow a predetermined threshold is indicative of a low riskatherosclerosis. Thus, in some embodiments, the system can be configuredto take into account the number and/or sides of a plaque. For example,if there are a higher number of plaques with smaller sides, then thatcan be associated with a higher surface area or more irregularity, whichin turn can be associated with a higher surface area to volume ratio. Incontrast, if there are fewer number of plaques with larger sides or moreregularity, then that can be associated with a lower surface area tovolume ratio or a higher volume to surface area ratio. In someembodiments, a high radiodensity value can indicate that a plaque ishighly calcified or stable, whereas a low radiodensity value canindicate that a plaque is less calcified or unstable. As such, in someembodiments, the system can be configured to determine that aradiodensity of a region of plaque above a predetermined threshold isindicative of a low risk atherosclerosis. In some embodiments, a plaquehaving a low heterogeneity or high homogeneity can indicate that theplaque is stable. As such, in some embodiments, the system can beconfigured to determine that a heterogeneity of a region of plaque belowa predetermined threshold is indicative of a low risk atherosclerosis.

In some embodiments, at block 342, the system is configured to calculateor determine a numerical calculation or representation of coronarystenosis based on the quantified and/or classified atherosclerosisderived from the medical image. In some embodiments, the system isconfigured to calculate stenosis using the one or more vascularmorphology parameters and/or quantified plaque parameters derived fromthe medical image of a coronary region of the subject.

In some embodiments, at block 344, the system is configured to predict arisk of ischemia for the subject based on the quantified and/orclassified atherosclerosis derived from the medical image. In someembodiments, the system is configured to calculate a risk of ischemiausing the one or more vascular morphology parameters and/or quantifiedplaque parameters derived from the medical image of a coronary region ofthe subject.

In some embodiments, the system is configured to generate a proposedtreatment for the subject based on the quantified and/or classifiedatherosclerosis, stenosis, and/or risk of ischemia, wherein all of theforegoing are derived automatically and/or dynamically from a rawmedical image using image processing algorithms and techniques.

In some embodiments, one or more processes described herein inconnection with FIG. 3A can be repeated. For example, if a medical imageof the same subject is taken again at a later point in time, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for tracking of quantified atherosclerosis for asubject and/or other purposes.

Quantification of Plaque, Stenosis, and/or CAD-RADS Score

As discussed herein, in some embodiments, the system is configured totake the guesswork out of interpretation of medical images and providesubstantially exact and/or substantially accurate calculations orestimates of stenosis percentage, atherosclerosis, and/or CoronaryArtery Disease - Reporting and Data System (CAD-RADS) score as derivedfrom a medical image. As such, in some embodiments, the system canenhance the reads of the imagers by providing comprehensive quantitativeanalyses that can improve efficiency, accuracy, and/or reproducibility.

FIG. 3C is a flowchart illustrating an overview of an exampleembodiment(s) of a method for quantification of stenosis and generationof a CAD-RADS score based on medical image analysis. As illustrated inFIG. 3A, in some embodiments, the system can be configured to access amedical image at block 202. Additional detail regarding the types ofmedical images and other processes and techniques represented in block202 can be found in the description above in relation to FIG. 2A.

In some embodiments, at block 354, the system is configured to identifyone or more arteries, plaque, and/or fat in the medical image, forexample using AI, ML, and/or other algorithms. The processes andtechniques for identifying one or more arteries, plaque, and/or fat caninclude one or more of the same features as described above in relationto blocks 204 and 206. In particular, in some embodiments, the systemcan be configured to utilize one or more AI and/or ML algorithms toautomatically and/or dynamically identify one or more arteries,including for example coronary arteries, carotid arteries, aorta, renalartery, lower extremity artery, and/or cerebral artery. In someembodiments, one or more AI and/or ML algorithms can be trained using aConvolutional Neural Network (CNN) on a set of medical images on whicharteries have been identified, thereby allowing the AI and/or MLalgorithm automatically identify arteries directly from a medical image.In some embodiments, the arteries are identified by size and/orlocation.

Further, in some embodiments, the system can be configured to identifyone or more regions of plaque in the medical image, for example usingone or more AI and/or ML algorithms to automatically and/or dynamicallyidentify one or more regions of plaque. In some embodiments, the one ormore AI and/or ML algorithms can be trained using a Convolutional NeuralNetwork (CNN) on a set of medical images on which regions of plaque havebeen identified, thereby allowing the AI and/or ML algorithmautomatically identify regions of plaque directly from a medical image.In some embodiments, the system can be configured to identify a vesselwall and a lumen wall for each of the identified coronary arteries inthe medical image. In some embodiments, the system is then configured todetermine the volume in between the vessel wall and the lumen wall asplaque. In some embodiments, the system can be configured to identifyregions of plaque based on the radiodensity values typically associatedwith plaque, for example by setting a predetermined threshold or rangeof radiodensity values that are typically associated with plaque with orwithout normalizing using a normalization device.

Similarly, in some embodiments, the system can be configured to identifyone or more regions of fat, such as epicardial fat, in the medicalimage, for example using one or more AI and/or ML algorithms toautomatically and/or dynamically identify one or more regions of fat. Insome embodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of medical images onwhich regions of fat have been identified, thereby allowing the AIand/or ML algorithm automatically identify regions of fat directly froma medical image. In some embodiments, the system can be configured toidentify regions of fat based on the radiodensity values typicallyassociated with fat, for example by setting a predetermined threshold orrange of radiodensity values that are typically associated with fat withor without normalizing using a normalization device.

In some embodiments, the system can be configured to determine one ormore vascular morphology and/or quantified plaque parameters at block208. For example, in some embodiments, the system can be configured todetermine a geometry and/or volume of a region of plaque and/or a vesselat block 201, a ratio or function of volume to surface area of a regionof plaque at block 203, a heterogeneity or homogeneity index of a regionof plaque at block 205, radiodensity of a region of plaque and/or acomposition thereof by ranges of radiodensity values at block 207, aratio of radiodensity to volume of a region of plaque at block 209,and/or a diffusivity of a region of plaque at block 211. Additionaldetail regarding the processes and techniques represented in blocks 208,201, 203, 205, 207, 209, and 211 can be found in the description abovein relation to FIG. 2A.

In some embodiments, at block 358, the system is configured to calculateor determine a numerical calculation or representation of coronarystenosis based on the one or more vascular morphology parameters and/orquantified plaque parameters derived from the medical image of acoronary region of the subject. In some embodiments, the system can beconfigured to generate a weighted measure of one or more vascularmorphology parameters and/or quantified plaque parameters determinedand/or derived from raw medical images. For example, in someembodiments, the system can be configured weight one or more vascularmorphology parameters and/or quantified plaque parameters equally. Insome embodiments, the system can be configured to weight one or morevascular morphology parameters and/or quantified plaque parametersdifferently. In some embodiments, the system can be configured weightone or more vascular morphology parameters and/or quantified plaqueparameters logarithmically, algebraically, and/or utilizing anothermathematical transform. In some embodiments, the system is configured tocalculate stenosis at block 358 using the weighted measure and/or usingonly some of the vascular morphology parameters and/or quantified plaqueparameters. In some embodiments, the system can be configured tocalculate stenosis on a vessel-by-vessel basis or a region-by-regionbasis.

In some embodiments, based on the calculated stenosis, the system isconfigured to determine a CAD-RADS score at block 360. This is incontrast to preexisting methods of determining a CAD-RADS based oneyeballing or general assessment of a medical image by a physician,which can result in unreproducible results. In some embodimentsdescribed herein, however, the system can be configured to generate areproducible and/or objective calculated CAD-RADS score based onautomatic and/or dynamic image processing of a raw medical image.

In some embodiments, at block 362, the system can be configured todetermine a presence or risk of ischemia based on the calculatedstenosis, one or more quantified plaque parameters and/or vascularmorphology parameters derived from the medical image. For example, insome embodiments, the system can be configured to determine a presenceor risk of ischemia by combining one or more of the foregoingparameters, either weighted or not, or by using some or all of theseparameters on an individual basis. In some embodiments, the system canbe configured to determine a presence of risk of ischemia by comparingone or more of the calculated stenosis, one or more quantified plaqueparameters and/or vascular morphology parameters to a database of knownsuch parameters derived from medical images of other subjects, includingfor example healthy subjects and/or subjects at risk of a cardiovascularevent. In some embodiments, the system can be configured to calculatepresence or risk of ischemia on a vessel-by-vessel basis or aregion-by-region basis.

In some embodiments, at block 364, the system can be configured todetermine one or more quantified parameters of fat for one or moreregions of fat identified from the medical image. For example, in someembodiments, the system can utilize any of the processes and/ortechniques discussed herein in relation to deriving quantifiedparameters of plaque, such as those described in connection with blocks208, 201, 203, 205, 207, 209, and 211. In particular, in someembodiments, the system can be configured to determine one or moreparameters of fat, including volume, geometry, radiodensity, and/or thelike of one or more regions of fat within the medical image.

In some embodiments, at block 366, the system can be configured togenerate a risk assessment of cardiovascular disease or event for thesubject. In some embodiments, the generated risk assessment can comprisea risk score indicating a risk of coronary disease for the subject. Insome embodiments, the system can generate a risk assessment based on ananalysis of one or more vascular morphology parameters, one or morequantified plaque parameters, one or more quantified fat parameters,calculated stenosis, risk of ischemia, CAD-RADS score, and/or the like.In some embodiments, the system can be configured to generate a weightedmeasure of one or more vascular morphology parameters, one or morequantified plaque parameters, one or more quantified fat parameters,calculated stenosis, risk of ischemia, and/or CAD-RADS score of thesubject. For example, in some embodiments, the system can be configuredweight one or more of the foregoing parameters equally. In someembodiments, the system can be configured weight one or more of theseparameters differently. In some embodiments, the system can beconfigured weight one or more of these parameters logarithmically,algebraically, and/or utilizing another mathematical transform. In someembodiments, the system is configured to generate a risk assessment ofcoronary disease or cardiovascular event for the subject at block 366using the weighted measure and/or using only some of these parameters.

In some embodiments, the system can be configured to generate a riskassessment of coronary disease or cardiovascular event for the subjectby combining one or more of the foregoing parameters, either weighted ornot, or by using some or all of these parameters on an individual basis.In some embodiments, the system can be configured to generate a riskassessment of coronary disease or cardiovascular event by comparing oneor more vascular morphology parameters, one or more quantified plaqueparameters, one or more quantified fat parameters, calculated stenosis,risk of ischemia, and/or CAD-RADS score of the subject to a database ofknown such parameters derived from medical images of other subjects,including for example healthy subjects and/or subjects at risk of acardiovascular event.

Further, in some embodiments, the system can be configured toautomatically and/or dynamically generate a CAD-RADS modifier based onone or more of the determined one or more vascular morphologyparameters, the set of quantified plaque parameters of the one or moreregions of plaque, the quantified coronary stenosis, the determinedpresence or risk of ischemia, and/or the determined set of quantifiedfat parameters. In particular, in some embodiments, the system can beconfigured to automatically and/or dynamically generate one or moreapplicable CAD-RADS modifiers for the subject, including for example oneor more of nondiagnostic (N), stent (S), graft (G), or vulnerability(V), as defined by and used by CAD-RADS. For example, N can indicatethat a study is nondiagnostic, S can indicate the presence of a stent, Gcan indicate the presence of a coronary artery bypass graft, and V canindicate the presence of vulnerable plaque, for example showing a lowradiodensity value.

In some embodiments, the system can be configured to generate a proposedtreatment for the subject based on the generated risk assessment ofcoronary disease, one or more vascular morphology parameters, one ormore quantified plaque parameters, one or more quantified fatparameters, calculated stenosis, risk of ischemia, CAD-RADS score,and/or CAD-RADS modifier derived from the raw medical image using imageprocessing.

In some embodiments, one or more processes described herein inconnection with FIG. 3B can be repeated. For example, if a medical imageof the same subject is taken again at a later point in time, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for tracking of quantified plaque, calculatedstenosis, CAD-RADS score and/or modifier derived from a medicalimage(s), risk determined risk of ischemia, quantified fat parameters,generated risk assessment of coronary disease for a subject, and/orother purposes.

Disease Tracking

In some embodiments, the systems, methods, and devices described hereincan be configured to track the progression and/or regression of anarterial and/or plaque-based disease, such as a coronary disease. Forexample, in some embodiments, the system can be configured to track theprogression and/or regression of a disease by automatically and/ordynamically analyzing a plurality of medical images obtained fromdifferent times using one or more techniques discussed herein andcomparing different parameters derived therefrom. As such, in someembodiments, the system can provide an automated disease tracking toolusing non-invasive raw medical images as an input, which does not relyon subjective assessment.

In particular, in some embodiments, the system can be configured toutilize a four-category system to determine whether plaque stabilizationor worsening is occurring in a subject. For example, in someembodiments, these categories can include: (1) “plaque progression” or“rapid plaque progression”; (2) “mixed response - calcium dominant” or“non-rapid calcium dominant mixed response”; (3) “mixed response -non-calcium dominant” or “non-rapid non-calcium dominant mixedresponse”; or (4) “plaque regression.”

In some embodiments, in “plaque progression” or “rapid plaqueprogression,” the overall volume or relative volume of plaque increases.In some embodiments, in “mixed response - calcium dominant” or“non-rapid calcium dominant mixed response,” the plaque volume remainsrelatively constant or does not increase to the threshold level of“rapid plaque progression” but there is a general progression ofcalcified plaque and a general regression of non-calcified plaque. Insome embodiments, in “mixed response - non-calcium dominant” or“non-rapid non-calcium dominant mixed response,” the plaque volumeremains relatively constant but there is a general progression ofnon-calcified plaque and a general regression of calcified plaque. Insome embodiments, in “plaque regression,” the overall volume or relativevolume of plaque decreases.

In some embodiments, these 4 categories can be expanded to be moregranular, for example including for higher vs. lower density calciumplaques (e.g., for those > vs. <1000 Hounsfield units) and/or tocategorize more specifically in calcium-dominant and non-calcifiedplaque-dominant mixed response. For example, for the non-calcifiedplaque-dominant mixed response, the non-calcified plaque can furtherinclude necrotic core, fibrofatty plaque and/or fibrous plaque asseparate categories within the overall umbrella of non-calcified plaque.Similarly, calcified plaques can be categorized as lower densitycalcified plaques, medium density calcified plaques and high densitycalcified plaques.

FIG. 3D is a flowchart illustrating an overview of an exampleembodiment(s) of a method for disease tracking based on medical imageanalysis. For example, in some embodiments, the system can be configuredto track the progression and/or regression of a plaque-based disease orcondition, such as a coronary disease relating to or involvingatherosclerosis, stenosis, ischemia, and/or the like, by analyzing oneor more medical images obtained non-invasively.

As illustrated in FIG. 3D, in some embodiments, the system at block 372is configured to access a first set of plaque parameters derived from amedical image of a subject at a first point in time. In someembodiments, the medical image can be stored in a medical image database100 and can include any of the types of medical images described above,including for example CT, non-contrast CT, contrast-enhanced CT, MR,DECT, Spectral CT, and/or the like. In some embodiments, the medicalimage of the subject can comprise the coronary region, coronaryarteries, carotid arteries, renal arteries, abdominal aorta, cerebralarteries, lower extremities, and/or upper extremities of the subject. Insome embodiments, the set of plaque parameters can be stored in a plaqueparameter database 370, which can include any of the quantified plaqueparameters discussed above in relation to blocks 208, 201, 203, 205,207, 209, and/or 211.

In some embodiments, the system can be configured to directly access thefirst set of plaque parameters that were previously derived from amedical image(s) and/or stored in a plaque parameter database 370. Insome embodiments, the plaque parameter database 370 can be locallyaccessible and/or remotely accessible by the system via a networkconnection. In some embodiments, the system can be configured todynamically and/or automatically derive the first set of plaqueparameters from a medical image taken from a first point in time.

In some embodiments, at block 374, the system can be configured toaccess a second medical image(s) of the subject, which can be obtainedfrom the subject at a later point in time than the medical image fromwhich the first set of plaque parameters were derived. In someembodiments, the medical image can be stored in a medical image database100 and can include any of the types of medical images described above,including for example CT, non-contrast CT, contrast-enhanced CT, MR,DECT, Spectral CT, and/or the like.

In some embodiments, at block 376, the system can be configured todynamically and/or automatically derive a second set of plaqueparameters from the second medical image taken from the second point intime. In some embodiments, the second set of plaque parameters caninclude any of the quantified plaque parameters discussed above inrelation to blocks 208, 201, 203, 205, 207, 209, and/or 211. In someembodiments, the system can be configured to store the derived ordetermined second set of plaque parameters in the plaque parameterdatabase 370.

In some embodiments, at block 378, the system can be configured toanalyze changes in one or more plaque parameters between the first setderived from a medical image taken at a first point in time to thesecond set derived from a medical image taken at a later point in time.For example, in some embodiments, the system can be configured tocompare a quantified plaque parameter between the two scans, such as forexample radiodensity, volume, geometry, location, ratio or function ofvolume to surface area, heterogeneity index, radiodensity composition,radiodensity composition as a function of volume, ratio of radiodensityto volume, diffusivity, any combinations or relations thereof, and/orthe like of one or more regions of plaque. In some embodiments, thesystem can be configured to determine the heterogeneity index of one ormore regions of plaque by generating a spatial mapping or athree-dimensional histogram of radiodensity values across a geometricshape of one or more regions of plaque. In some embodiments, the systemis configured to analyze changes in one or more non-image based metrics,such as for example serum biomarkers, genetics, omics, transcriptomics,microbiomics, and/or metabolomics.

In some embodiments, the system is configured to determine a change inplaque composition in terms of radiodensity or stable v. unstable plaquebetween the two scans. For example, in some embodiments, the system isconfigured to determine a change in percentage of higher radiodensity orstable plaques v. lower radiodensity or unstable plaques between the twoscans. In some embodiments, the system can be configured to track achange in higher radiodensity plaques v. lower radiodensity plaquesbetween the two scans. In some embodiments, the system can be configuredto define higher radiodensity plaques as those with a Hounsfield unit ofabove 1000 and lower radiodensity plaques as those with a Hounsfieldunit of below 1000.

In some embodiments, at block 380, the system can be configured todetermine the progression or regression of plaque and/or any otherrelated measurement, condition, assessment, or related disease based onthe comparison of the one or more parameters derived from two or morescans and/or change in one or more non-image based metrics, such asserum biomarkers, genetics, omics, transcriptomics, microbiomics, and/ormetabolomics. For example, in some embodiments, the system can beconfigured to determine the progression and/or regression of plaque ingeneral, atherosclerosis, stenosis, risk or presence of ischemia, and/orthe like. Further, in some embodiments, the system can be configured toautomatically and/or dynamically generate a CAD-RADS score of thesubject based on the quantified or calculated stenosis, as derived fromthe two medical images. Additional detail regarding generating aCAD-RADS score is described herein in relation to FIG. 3C. In someembodiments, the system can be configured to determine a progression orregression in the CAD-RADS score of the subject. In some embodiments,the system can be configured to compare the plaque parametersindividually and/or combining one or more of them as a weighted measure.For example, in some embodiments, the system can be configured to weightthe plaque parameters equally, differently, logarithmically,algebraically, and/or utilizing another mathematical transform. In someembodiments, the system can be configured to utilize only some or all ofthe quantified plaque parameters.

In some embodiments, the state of plaque progression as determined bythe system can include one of four categories, including rapid plaqueprogression, non-rapid calcium dominant mixed response, non-rapidnon-calcium dominant mixed response, or plaque regression. In someembodiments, the system is configured to classify the state of plaqueprogression as rapid plaque progression when a percent atheroma volumeincrease of the subject is more than 1% per year. In some embodiments,the system is configured to classify the state of plaque progression asnon-rapid calcium dominant mixed response when a percent atheroma volumeincrease of the subject is less than 1% per year and calcified plaquerepresents more than 50% of total new plaque formation. In someembodiments, the system is configured to classify the state of plaqueprogression as non-rapid non-calcium dominant mixed response when apercent atheroma volume increase of the subject is less than 1% per yearand non-calcified plaque represents more than 50% of total new plaqueformation. In some embodiments, the system is configured to classify thestate of plaque progression as plaque regression when a decrease intotal percent atheroma volume is present.

In some embodiments, at block 382, the system can be configured togenerate a proposed treatment plan for the subject. For example, in someembodiments, the system can be configured to generate a proposedtreatment plan for the subject based on the determined progression orregression of plaque and/or any other related measurement, condition,assessment, or related disease based on the comparison of the one ormore parameters derived from two or more scans.

In some embodiments, one or more processes described herein inconnection with FIG. 3D can be repeated. For example, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for continued tracking of a plaque-based diseaseand/or other purposes.

Determination of Cause of Change in Calcium

In some embodiments, the systems, methods and devices disclosed hereincan be configured to generate analysis and/or reports that can determinethe likely cause of an increased calcium score. A high or increasedcalcium score alone is not representative of any specific cause, eitherpositive or negative. Rather, in general, there can be various possiblecauses for a high or increased calcium score. For example, in somecases, a high or increased calcium score can be an indicator ofsignificant heart disease and/or that the patient is at increased riskof a heart attack. Also, in some cases, a high or increased calciumscore can be an indicator that the patient is increasing the amount ofexercise performed, because exercise can convert fatty material plaquewithin the artery vessel. In some cases, a high or increased calciumscore can be an indicator of the patient beginning a statin regimenwherein the statin is converting the fatty material plaque into calcium.Unfortunately, a blood test alone cannot be used to determine which ofthe foregoing reasons is the likely cause of an increased calcium score.In some embodiments, by utilizing one or more techniques describedherein, the system can be configured to determine the cause of anincreased or high calcium score.

More specifically, in some embodiments, the system can be configured totrack a particular segment of an artery wall vessel of a patient in sucha way to monitor the conversion of a fatty deposit material plaquelesion to a mostly calcified plaque deposit, which can be helpful indetermining the cause of an increase calcium score, such as one or moreof the causes identified above. In addition, in some embodiments, thesystem can be configured to determine and/or use the location, size,shape, diffusivity and/or the attenuation radiodensity of one or moreregions of calcified plaque to determine the cause of an increase incalcium score. As a non-limiting example, if a calcium plaque increasesin density, this may represent a stabilization of plaque by treatment orlifestyle, whereas if a new calcium plaque forms where one was not therebefore (particularly with a lower attenuation density), this mayrepresent an adverse finding of disease progression rather thanstabilization. In some embodiments, one or more processes and techniquesdescribed herein may be applied for non-contrast CT scans (such as anECG gated coronary artery calcium score or non-ECG gated chest CT) aswell as contrast-enhanced CT scans (such as a coronary CT angiogram).

As another non-limiting example, the CT scan image acquisitionparameters can be altered to improve understanding of calcium changesover time. As an example, traditional coronary artery calcium imaging isdone using a 2.5-3.0 mm slice thickness and detecting voxels/pixels thatare 130 Hounsfield units or greater. An alternative may be to do “thin”slice imaging with 0.5 mm slice thickness or similar; and detecting allHounsfield units densities below 130 and above a certain threshold(e.g., 100) that may identify less dense calcium that may be missed byan arbitrary 130 Hounsfield unit threshold.

FIG. 3E is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of cause of change incalcium score, whether an increase or decrease, based on medical imageanalysis.

As illustrated in FIG. 3E, in some embodiments, the system can beconfigured to access a first calcium score and/or a first set of plaqueparameters of a subject at block 384. The first calcium score and/or afirst set of plaque parameters can be derived from a medical image of asubject and/or from a blood test at a first point in time. In someembodiments, the medical image can be stored in a medical image database100 and can include any of the types of medical images described above,including for example CT, non-contrast CT, contrast-enhanced CT, MR,DECT, Spectral CT, and/or the like. In some embodiments, the medicalimage of the subject can comprise the coronary region, coronaryarteries, carotid arteries, renal arteries, abdominal aorta, cerebralarteries, lower extremities, and/or upper extremities of the subject. Insome embodiments, the set of plaque parameters can be stored in a plaqueparameter database 370, which can include any of the quantified plaqueparameters discussed above in relation to blocks 208, 201, 203, 205,207, 209, and/or 211.

In some embodiments, the system can be configured to directly accessand/or retrieve the first calcium score and/or first set of plaqueparameters that are stored in a calcium score database 398 and/or plaqueparameter database 370 respectively. In some embodiments, the plaqueparameter database 370 and/or calcium score database 298 can be locallyaccessible and/or remotely accessible by the system via a networkconnection. In some embodiments, the system can be configured todynamically and/or automatically derive the first set of plaqueparameters and/or calcium score from a medical image and/or blood testof the subject taken from a first point in time.

In some embodiments, at block 386, the system can be configured toaccess a second calcium score and/or second medical image(s) of thesubject, which can be obtained from the subject at a later point in timethan the first calcium score and/or medical image from which the firstset of plaque parameters were derived. For example, in some embodiments,the second calcium score can be derived from the second medical imageand/or a second blood test taken of the subject at a second point intime. In some embodiments, the second calcium score can be stored in thecalcium score database 398. In some embodiments, the medical image canbe stored in a medical image database 100 and can include any of thetypes of medical images described above, including for example CT,non-contrast CT, contrast-enhanced CT, MR, DECT, Spectral CT, and/or thelike.

In some embodiments, at block 388, the system can be configured tocompare the first calcium score to the second calcium score anddetermine a change in the calcium score. However, as discussed above,this alone typically does not provide insight as to the cause of thechange in calcium score, if any. In some embodiments, if there is nostatistically significant change in calcium score between the tworeadings, for example if any difference is below a predeterminedthreshold value, then the system can be configured to end the analysisof the change in calcium score. In some embodiments, if there is astatistically significant change in calcium score between the tworeadings, for example if the difference is above a predeterminedthreshold value, then the system can be configured to continue itsanalysis.

In particular, in some embodiments, at block 390, the system can beconfigured to dynamically and/or automatically derive a second set ofplaque parameters from the second medical image taken from the secondpoint in time. In some embodiments, the second set of plaque parameterscan include any of the quantified plaque parameters discussed above inrelation to blocks 208, 201, 203, 205, 207, 209, and/or 211. In someembodiments, the system can be configured to store the derived ordetermined second set of plaque parameters in the plaque parameterdatabase 370.

In some embodiments, at block 392, the system can be configured toanalyze changes in one or more plaque parameters between the first setderived from a medical image taken at a first point in time to thesecond set derived from a medical image taken at a later point in time.For example, in some embodiments, the system can be configured tocompare a quantified plaque parameter between the two scans, such as forexample radiodensity, volume, geometry, location, ratio or function ofvolume to surface area, heterogeneity index, radiodensity composition,radiodensity composition as a function of volume, ratio of radiodensityto volume, diffusivity, any combinations or relations thereof, and/orthe like of one or more regions of plaque and/or one or more regionssurrounding plaque. In some embodiments, the system can be configured todetermine the heterogeneity index of one or more regions of plaque bygenerating a spatial mapping or a three-dimensional histogram ofradiodensity values across a geometric shape of one or more regions ofplaque. In some embodiments, the system is configured to analyze changesin one or more non-image based metrics, such as for example serumbiomarkers, genetics, omics, transcriptomics, microbiomics, and/ormetabolomics.

In some embodiments, the system is configured to determine a change inplaque composition in terms of radiodensity or stable v. unstable plaquebetween the two scans. For example, in some embodiments, the system isconfigured to determine a change in percentage of higher radiodensity orstable plaques v. lower radiodensity or unstable plaques between the twoscans. In some embodiments, the system can be configured to track achange in higher radiodensity plaques v. lower radiodensity plaquesbetween the two scans. In some embodiments, the system can be configuredto define higher radiodensity plaques as those with a Hounsfield unit ofabove 1000 and lower radiodensity plaques as those with a Hounsfieldunit of below 1000.

In some embodiments, the system can be configured to compare the plaqueparameters individually and/or combining one or more of them as aweighted measure. For example, in some embodiments, the system can beconfigured to weight the plaque parameters equally, differently,logarithmically, algebraically, and/or utilizing another mathematicaltransform. In some embodiments, the system can be configured to utilizeonly some or all of the quantified plaque parameters.

In some embodiments, at block 394, the system can be configured tocharacterize the change in calcium score of the subject based on thecomparison of the one or more plaque parameters, whether individuallyand/or combined or weighted. In some embodiments, the system can beconfigured to characterize the change in calcium score as positive,neutral, or negative. For example, in some embodiments, if thecomparison of one or more plaque parameters reveals that plaque isstabilizing or showing high radiodensity values as a whole for thesubject without generation of any new plaque, then the system can reportthat the change in calcium score is positive. In contrast, if thecomparison of one or more plaque parameters reveals that plaque isdestabilizing as a whole for the subject, for example due to generationof new unstable regions of plaque with low radiodensity values, withoutgeneration of any new plaque, then the system can report that the changein calcium score is negative. In some embodiments, the system can beconfigured to utilize any or all techniques of plaque quantificationand/or tracking of plaque-based disease analysis discussed herein,include those discussed in connection with FIGS. 3A, 3B, 3C, and 3D.

As a non-limiting example, in some embodiments, the system can beconfigured to characterize the cause of a change in calcium score basedon determining and comparing a change in ratio between volume andradiodensity of one or more regions of plaque between the two scans.Similarly, in some embodiments, the system can be configured tocharacterize the cause of a change in calcium score based on determiningand comparing a change in diffusivity and/or radiodensity of one or moreregions of plaque between the two scans. For example, if theradiodensity of a region of plaque has increased, the system can beconfigured to characterize the change or increase in calcium score aspositive. In some embodiments, if the system identifies one or more newregions of plaque in the second image that were not present in the firstimage, the system can be configured to characterize the change incalcium score as negative. In some embodiments, if the system determinesthat the volume to surface area ratio of one or more regions of plaquehas decreased between the two scans, the system can be configured tocharacterize the change in calcium score as positive. In someembodiments, if the system determines that a heterogeneity orheterogeneity index of a region is plaque has decreased between the twoscans, for example by generating and/or analyzing spatial mapping ofradiodensity values, then the system can be configured to characterizethe change in calcium score as positive.

In some embodiments, the system is configured to utilize an AI, ML,and/or other algorithm to characterize the change in calcium score basedon one or more plaque parameters derived from a medical image. Forexample, in some embodiments, the system can be configured to utilize anAI and/or ML algorithm that is trained using a CNN and/or using adataset of known medical images with identified plaque parameterscombined with calcium scores. In some embodiments, the system can beconfigured to characterize a change in calcium score by accessing knowndatasets of the same stored in a database. For example, the knowndataset may include datasets of changes in calcium scores and/or medicalimages and/or plaque parameters derived therefrom of other subjects inthe past. In some embodiments, the system can be configured tocharacterize a change in calcium score and/or determine a cause thereofon a vessel-by-vessel basis, segment-by-segment basis, plaque-by-plaquebasis, and/or a subject basis.

In some embodiments, at block 396, the system can be configured togenerate a proposed treatment plan for the subject. For example, in someembodiments, the system can be configured to generate a proposedtreatment plan for the subject based on the change in calcium scoreand/or characterization thereof for the subject.

In some embodiments, one or more processes described herein inconnection with FIG. 3E can be repeated. For example, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for continued tracking and/or characterization ofchanges in calcium score for a subject and/or other purposes.

Prognosis of Cardiovascular Event

In some embodiments, the systems, devices, and methods described hereinare configured to generate a prognosis of a cardiovascular event for asubject based on one or more of the medical image-based analysistechniques described herein. For example, in some embodiments, thesystem is configured to determine whether a patient is at risk for acardiovascular event based on the amount of bad plaque buildup in thepatient’s artery vessels. For this purpose, a cardiovascular event caninclude clinical major cardiovascular events, such as heart attack,stroke or death, as well as disease progression and/or ischemia.

In some embodiments, the system can identify the risk of acardiovascular event based on a ratio of the amount and/or volume of badplaque buildup versus the total surface area and/or volume of some orall of the artery vessels in a patient. In some embodiments, if theforegoing ratio exceeds a certain threshold, the system can beconfigured to output a certain risk factor and/or number and/or levelassociated with the patient. In some embodiments, the system isconfigured to determine whether a patient is at risk for acardiovascular event based on an absolute amount or volume or a ratio ofthe amount or volume bad plaque buildup in the patient’s artery vesselscompared to the total volume of some or all of the artery vessels. Insome embodiments, the system is configured to determine whether apatient is at risk for a cardiovascular event based on results fromblood chemistry or biomarker tests of the patient, for example whethercertain blood chemistry or biomarker tests of the patient exceed certainthreshold levels. In some embodiments, the system is configured toreceive as input from the user or other systems and/or access bloodchemistry or biomarker tests data of the patient from a database system.In some embodiments, the system can be configured to utilize not onlyartery information related to plaque, vessel morphology, and/or stenosisbut also input from other imaging data about the non-coronarycardiovascular system, such as subtended left ventricular mass, chambervolumes and size, valvular morphology, vessel (e.g., aorta, pulmonaryartery) morphology, fat, and/or lung and/or bone health. In someembodiments, the system can utilize the outputted risk factor togenerate a treatment plan proposal. For example, the system can beconfigured to output a treatment plan that involves the administrationof cholesterol reducing drugs, such as statins, in order to transformthe soft bad plaque into hard plaque that is safer and more stable for apatient. In general, hard plaque that is largely calcified can have asignificant lower risk of rupturing into the artery vessel therebydecreasing the chances of a clot forming in the artery vessel which candecrease a patient’s risk of a heart attack or other cardiac event.

FIG. 4A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for prognosis of a cardiovascular event basedon and/or derived from medical image analysis.

As illustrated in FIG. 4A, in some embodiments, the system can beconfigured to access a medical image at block 202, such as a CT scan ofa coronary region of a subject, which can be stored in a medical imagedatabase 100. Further, in some embodiments, the system can be configuredto identify one or more arteries at block 204 and/or one or more regionsof plaque at block 206. In addition, in some embodiments, the system canbe configured to determine one or more vascular morphology and/orquantified plaque parameters at block 208. For example, in someembodiments, the system can be configured to determine a geometry and/orvolume of a region of plaque and/or a vessel, a ratio or function ofvolume to surface area of a region of plaque, a heterogeneity orhomogeneity index of a region of plaque, radiodensity of a region ofplaque and/or a composition thereof by ranges of radiodensity values, aratio of radiodensity to volume of a region of plaque, and/or adiffusivity of a region of plaque. In addition, in some embodiments, atblock 210, the system can be configured to classify one or more regionsof plaque as stable v. unstable or good v. bad based on the one or morevascular morphology parameters and/or quantified plaque parametersdetermined and/or derived from raw medical images. Additional detailregarding the processes and techniques represented in blocks 202, 204,206, 208, and 210 can be found in the description above in relation toFIG. 2A.

In some embodiments, the system at block 412 is configured to generate aratio of bad plaque to the vessel on which the bad plaque appears. Morespecifically, in some embodiments, the system can be configured todetermine a total surface area of a vessel identified on a medical imageand a surface area of all regions of bad or unstable plaque within thatvessel. Based on the foregoing, in some embodiments, the system can beconfigured to generate a ratio of surface area of all bad plaque withina particular vessel to the surface area of the entire vessel or aportion thereof shown in a medical image. Similarly, in someembodiments, the system can be configured to determine a total volume ofa vessel identified on a medical image and a volume of all regions ofbad or unstable plaque within that vessel. Based on the foregoing, insome embodiments, the system can be configured to generate a ratio ofvolume of all bad plaque within a particular vessel to the volume of theentire vessel or a portion thereof shown in a medical image.

In some embodiments, at block 414, the system is further configured todetermine a total absolute volume and/or surface area of all bad orunstable plaque identified in a medical image. Also, in someembodiments, at block 416, the system is configured to determine a totalabsolute volume of all plaque, including good plaque and bad plaque,identified in a medical image. Further, in some embodiments, at block418, the system can be configured to access or retrieve results from ablood chemistry and/or biomarker test of the patient and/or othernon-imaging test results. Furthermore, in some embodiments, at block422, the system can be configured to access and/or analyze one or morenon-coronary cardiovascular system medical images.

In some embodiments, at block 420, the system can be configured toanalyze one or more of the generated ratio of bad plaque to a vessel,whether by surface area or volume, total absolute volume of bad plaque,total absolute volume of plaque, blood chemistry and/or biomarker testresults, and/or analysis results of one or more non-coronarycardiovascular system medical images to determine whether one or more ofthese parameters, either individually and/or combined, is above apredetermined threshold. For example, in some embodiments, the systemcan be configured to analyze one or more of the foregoing parametersindividually by comparing them to one or more reference values ofhealthy subjects and/or subjects at risk of a cardiovascular event. Insome embodiments, the system can be configured to analyze a combination,such as a weighted measure, of one or more of the foregoing parametersby comparing the combined or weighted measure thereof to one or morereference values of healthy subjects and/or subjects at risk of acardiovascular event. In some embodiments, the system can be configuredto weight one or more of these parameters equally. In some embodiments,the system can be configured to weight one or more of these parametersdifferently. In some embodiments, the system can be configured to weightone or more of these parameters logarithmically, algebraically, and/orutilizing another mathematical transform. In some embodiments, thesystem can be configured to utilize only some of the aforementionedparameters, either individually, combined, and/or as part of a weightedmeasure.

In some embodiments, at block 424, the system is configured to generatea prognosis for a cardiovascular event for the subject. In particular,in some embodiments, the system is configured to generate a prognosisfor cardiovascular event based on one or more of the analysis results ofthe generated ratio of bad plaque to a vessel, whether by surface areaor volume, total absolute volume of bad plaque, total absolute volume ofplaque, blood chemistry and/or biomarker test results, and/or analysisresults of one or more non-coronary cardiovascular system medicalimages. In some embodiments, the system is configured to generate theprognosis utilizing an AI, ML, and/or other algorithm. In someembodiments, the generated prognosis comprises a risk score or riskassessment of a cardiovascular event for the subject. In someembodiments, the cardiovascular event can include one or more ofatherosclerosis, stenosis, ischemia, heart attack, and/or the like.

In some embodiments, at block 426, the system can be configured togenerate a proposed treatment plan for the subject. For example, in someembodiments, the system can be configured to generate a proposedtreatment plan for the subject based on the change in calcium scoreand/or characterization thereof for the subject. In some embodiments,the generated treatment plan can include use of statins, lifestylechanges, and/or surgery.

In some embodiments, one or more processes described herein inconnection with FIG. 4A can be repeated. For example, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for continued prognosis of a cardiovascular eventfor a subject and/or other purposes.

Patient-Specific Stent Determination

In some embodiments, the systems, methods, and devices described hereincan be used to determine and/or generate one or more parameters for apatient-specific stent and/or selection or guidance for implantationthereof. In particular, in some embodiments, the systems disclosedherein can be used to dynamically and automatically determine thenecessary stent type, length, diameter, gauge, strength, and/or anyother stent parameter for a particular patient based on processing ofthe medical image data, for example using AI, ML, and/or otheralgorithms.

In some embodiments, by determining one or more patient-specific stentparameters that are best suited for a particular artery area, the systemcan reduce the risk of patient complications and/or insurance risksbecause if too large of a stent is implanted, then the artery wall canbe stretched too thin resulting in a possible rupture, or undesirablehigh flow, or other issues. On the other hand, if too small of a stentis implanted, then the artery wall might not be stretched open enoughresulting in too little blood flow or other issues.

In some embodiments, the system is configured to dynamically identify anarea of stenosis within an artery, dynamically determine a properdiameter of the identified area of the artery, and/or automaticallyselect a stent from a plurality of available stent options. In someembodiments, the selected stent can be configured to prop open theartery area after implantation to the determined proper artery diameter.In some embodiments, the proper artery diameter is determined to beequivalent or substantially equivalent to what the diameter wouldnaturally be without stenosis. In some embodiments, the system can beconfigured to dynamically generate a patient-specific surgical plan forimplanting the selected stent in the identified artery area. Forexample, the system can be configured to determine whether a bifurcationof the artery is near the identified artery area and generate apatient-specific surgical plan for inserting two guidewires for handlingthe bifurcation and/or determining the position for jailing andinserting a second stent into the bifurcation.

FIG. 4B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of patient-specific stentparameters based on medical image analysis.

As illustrated in FIG. 4B, in some embodiments, the system can beconfigured to access a medical image at block 202, such as a CT scan ofa coronary region of a subject. Further, in some embodiments, the systemcan be configured to identify one or more arteries at block 204 and/orone or more regions of plaque at block 206. In addition, in someembodiments, the system can be configured to determine one or morevascular morphology and/or quantified plaque parameters at block 208.For example, in some embodiments, the system can be configured todetermine a geometry and/or volume of a region of plaque and/or a vesselat block 201, a ratio or function of volume to surface area of a regionof plaque at block 203, a heterogeneity or homogeneity index of a regionof plaque at block 205, radiodensity of a region of plaque and/or acomposition thereof by ranges of radiodensity values at block 207, aratio of radiodensity to volume of a region of plaque at block 209,and/or a diffusivity of a region of plaque at block 211. Additionaldetail regarding the processes and techniques represented in blocks 202,204, 206, 208, 201, 203, 205, 207, 209, and 211 can be found in thedescription above in relation to FIG. 2A.

In some embodiments, at block 440, the system can be configured toanalyze the medical image to determine one or more vessel parameters,such as the diameter, curvature, vascular morphology, vessel wall, lumenwall, and/or the like. In some embodiments, the system can be configuredto determine or derive from the medical image one or more vesselparameters as shown in the medical image, for example with stenosis atcertain regions along the vessel. In some embodiments, the system can beconfigured to determine one or more vessel parameters without stenosis.For example, in some embodiments, the system can be configured tographically and/or hypothetically remove stenosis or plaque from avessel to determine the diameter, curvature, and/or the like of thevessel if stenosis did not exist.

In some embodiments, at block 442, the system can be configured todetermine whether a stent is recommended for the subject and, if so, oneor more recommended parameters of a stent specific for that patientbased on the medical analysis. For example, in some embodiments, thesystem can be configured to analyze one or more of the identifiedvascular morphology parameters, quantified plaque parameters, and/orvessel parameters. In some embodiments, the system can be configured toutilize an AI, ML, and/or other algorithm. In some embodiments, thesystem is configured to analyze one or more of the aforementionedparameters individually, combined, and/or as a weighted measure. In someembodiments, one or more of these parameters derived from a medicalimage, either individually or combined, can be compared to one or morereference values derived or collected from other subjects, includingthose who had a stent implanted and those who did not. In someembodiments, based on the determined parameters of a patient-specificstent, the system can be configured to determine a selection of apreexisting stent that matches those parameters and/or generatemanufacturing instructions to manufacture a patient-specific stent withstent parameters derived from a medical image. In some embodiments, thesystem can be configured to recommend a diameter of a stent that is lessthan or substantially equal to the diameter of an artery if stenosis didnot exist.

In some embodiments, at block 444, the system can be configured togenerate a recommended surgical plan for stent implantation based on theanalyzed medical image. For example, in some embodiments, the system canbe configured to determine whether a bifurcation exists based on themedical image and/or generate guidelines for the positioning ofguidewires and/or stent for the patient prior to surgery. As such, insome embodiments, the system can be configured to generate a detailedsurgical plan that is specific to a particular patient based on medicalimage analysis of plaque and/or other parameters.

In some embodiments, at block 446, the system is configured to access orretrieve one or more medical images after stent implantation. In someembodiments, at block 448, the system can be configured to analyze theaccessed medical image to perform post-implantation analysis. Forexample, in some embodiments, the system can be configured to derive oneor more vascular morphology and/or plaque parameters, including any ofthose discussed herein in relation to block 208, after stentimplantation. Based on analysis of the foregoing, in some embodiments,the system can generate further proposed treatment in some embodiments,such as for example recommended use of statins or other medications,lifestyle change, further surgery or stent implantation, and/or thelike.

In some embodiments, one or more processes described herein inconnection with FIG. 4B can be repeated. For example, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used to determine the need for and/or parameters of anadditional patient-specific stent for a patient and/or other purposes.

Patient-Specific Report

In some embodiments, the system is configured to dynamically generate apatient-specific report based on the analysis of the processed datagenerated from the raw CT scan data. In some embodiments, the patientspecific report is dynamically generated based on the processed data. Insome embodiments, the written report is dynamically generated based onselecting and/or combining certain phrases from a database, whereincertain words, terms, and/or phrases are altered to be specific to thepatient and the identified medical issues of the patient. In someembodiments, the system is configured to dynamically select one or moreimages from the image scanning data and/or the system generated imageviews described herein, wherein the selected one or more images aredynamically inserted into the written report in order to generate apatient-specific report based on the analysis of the processed data.

In some embodiments, the system is configured to dynamically annotatethe selected one or more images for insertion into the patient specificreport, wherein the annotations are specific to patient and/or areannotations based on the data processing performed by the devices,methods, and systems disclosed herein, for example, annotating the oneor more images to include markings or other indicators to show wherealong the artery there exists bad plaque buildup that is significant.

In some embodiments, the system is configured to dynamically generate areport based on past and/or present medical data. For example, in someembodiments, the system can be configured to show how a patient’scardiovascular health has changed over a period. In some embodiments,the system is configured to dynamically generate phrases and/or selectphrases from a database to specifically describe the cardiovascularhealth of the patient and/or how the cardiovascular disease has changedwithin a patient.

In some embodiments, the system is configured to dynamically select oneor more medical images from prior medical scanning and/or currentmedical scanning for insertion into the medical report in order to showhow the cardiovascular disease has changed over time in a patient, forexample, showing past and present images juxtaposed to each other, orfor example, showing past images that are superimposed on present imagesthereby allowing a user to move or fade or toggle between past andpresent images.

In some embodiments, the patient-specific report is an interactivereport that allows a user to interact with certain images, videos,animations, augmented reality (AR), virtual reality (VR), and/orfeatures of the report. In some embodiments, the system is configured toinsert into the patient-specific report dynamically generatedillustrations or images of patient artery vessels in order to highlightspecific vessels and/or portions of vessels that contain or are likelyto contain vascular disease that require review or further analysis. Insome embodiments, the dynamically generated patient-specific report isconfigured to show a user the vessel walls using AR and/or VR.

In some embodiments, the system is configured to insert into thedynamically generated report any ratios and/or dynamically generateddata using the methods, systems, and devices disclosed herein. In someembodiments, the dynamically generated report comprises a radiologyreport. In some embodiments, the dynamically generated report is in aneditable document, such as Microsoft Word®, in order to allow thephysician to make edits to the report. In some embodiments, thedynamically generated report is saved into a PACS (Picture Archiving andCommunication System) or other EMR (electronic medical records) system.

In some embodiments, the system is configured to transform and/ortranslate data from the imaging into drawings or infographics in a videoformat, with or without audio, in order to transmit accurately theinformation in a way that is better understandable to any patient toimprove literacy. In some embodiments, this method of improving literacyis coupled to a risk stratification tool that defines a lower risk withhigher literacy, and a higher risk with lower literacy. In someembodiments, these report outputs may be patient-derived and/orpatient-specific. In some embodiments, real patient imaging data (forexample, from their CT) can be coupled to graphics from their CT and/ordrawings from the CT to explain the findings further. In someembodiments, real patient imaging data, graphics data and/or drawingsdata can be coupled to an explaining graphic that is not from thepatient but that can help the patient better understand (for example, avideo about lipid-rich plaque).

In some embodiments, these patient reports can be imported into anapplication that allows for following disease over time in relation tocontrol of heart disease risk factors, such as diabetes or hypertension.In some embodiments, an app and/or user interface can allow forfollowing of blood glucose and blood pressure over time and/or relatethe changes of the image over time in a way that augments riskprediction.

In some embodiments, the system can be configured to generate a videoreport that is specific to the patient based on the processed datagenerated from the raw CT data. In some embodiments, the system isconfigured to generate and/or provide a personalized cinematic viewingexperience for a user, which can be programmed to automatically anddynamically change content based upon imaging findings, associatedauto-calculated diagnoses, and/or prognosis algorithms. In someembodiments, the method of viewing, unlike traditional reporting, isthrough a movie experience which can be in the form of a regular 2Dmovie and/or through a mixed reality movie experience through AR or VR.In some embodiments, in the case of both 2D and mixed reality, thepersonalized cinematic experience can be interactive with the patient topredict their prognosis, such as risk of heart attack, rate of diseaseprogression, and/or ischemia.

In some embodiments, the system can be configured to dynamicallygenerate a video report that comprises both cartoon images and/oranimation along with audio content in combination with actual CT imagedata from the patient. In some embodiments, the dynamically generatedvideo medical report is dynamically narrated based on selecting phrases,terms and/or other content from a database such that a voice synthesizeror pre-made voice content can be used for playback during the videoreport. In some embodiments, the dynamically generated video medicalreport is configured to comprise any of the images disclosed herein. Insome embodiments, the dynamically generated video medical report can beconfigured to dynamically select one or more medical images from priormedical scanning and/or current medical scanning for insertion into thevideo medical report in order to show how the cardiovascular disease haschanged over time in a patient. For example, in some embodiments, thereport can show past and present images juxtaposed next to each other.In some embodiments, the report can show past images that aresuperimposed on present images thereby allowing a user to toggle or moveor fade between past and present images. In some embodiments, thedynamically generated video medical report can be configured to showactual medical images, such as a CT medical image, in the video reportand then transition to an illustrative view or cartoon view (partial orentirely an illustrative or cartoon view) of the actual medical images,thereby highlighting certain features of the patient’s arteries. In someembodiments, the dynamically generated video medical report isconfigured to show a user the vessel walls using AR and/or VR.

FIG. 5A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for generation of a patient-specific medicalreport based on medical image analysis. As illustrated in FIG. 5A, insome embodiments, the system can be configured to access a medical imageat block 202. In some embodiments, the medical image can be stored in amedical image database 100. Additional detail regarding the types ofmedical images and other processes and techniques represented in block202 can be found in the description above in relation to FIG. 2A.

In some embodiments, at block 354, the system is configured to identifyone or more arteries, plaque, and/or fat in the medical image, forexample using AI, ML, and/or other algorithms. Additional detailregarding the types of medical images and other processes and techniquesrepresented in block 354 can be found in the description above inrelation to FIG. 3C.

In some embodiments, at block 208, the system can be configured todetermine one or more vascular morphology and/or quantified plaqueparameters. For example, in some embodiments, the system can beconfigured to determine a geometry and/or volume of a region of plaqueand/or a vessel at block 201, a ratio or function of volume to surfacearea of a region of plaque at block 203, a heterogeneity or homogeneityindex of a region of plaque at block 205, radiodensity of a region ofplaque and/or a composition thereof by ranges of radiodensity values atblock 207, a ratio of radiodensity to volume of a region of plaque atblock 209, and/or a diffusivity of a region of plaque at block 211.Additional detail regarding the processes and techniques represented inblocks 208, 201, 203, 205, 207, 209, and 211 can be found in thedescription above in relation to FIG. 2A.

In some embodiments, at block 508, the system can be configured todetermine and/or quantify stenosis, atherosclerosis, risk of ischemia,risk of cardiovascular event or disease, and/or the like. The system canbe configured to utilize any techniques and/or algorithms describedherein, including but not limited to those described above in connectionwith block 358 and block 366 of FIG. 3C.

In some embodiments, at block 510, the system can be configured togenerate an annotated medical image and/or quantized color map using theanalysis results derived from the medical image. For example, in someembodiments, the system can be configured to generate a quantized mapshowing one or more arteries, plaque, fat, good plaque, bad plaque,vascular morphologies, and/or the like.

In some embodiments, at block 512, the system can be configured todetermine a progression of plaque and/or disease of the patient, forexample based on analysis of previously obtained medical images of thesubject. In some embodiments, the system can be configured to utilizeany algorithms or techniques described herein in relation to diseasetracking, including but not limited to those described in connectionwith block 380 and/or FIG. 3D generally.

In some embodiments, at block 514, the system can be configured togenerate a proposed treatment plan for the patient based on thedetermined progression of plaque and/or disease. In some embodiments,the system can be configured to utilize any algorithms or techniquesdescribed herein in relation to disease tracking and treatmentgeneration, including but not limited to those described in connectionwith block 382 and/or FIG. 3D generally.

In some embodiments, at block 516, the system can be configured togenerate a patient-specific report. The patient-specific report caninclude one or more medical images of the patient and/or derivedgraphics thereof. For example, in some embodiments, the patient reportcan include one or more annotated medical images and/or quantized colormaps. In some embodiments, the patient-specific report can include oneor more vascular morphology and/or quantified plaque parameters derivedfrom the medical image. In some embodiments, the patient-specific reportcan include quantified stenosis, atherosclerosis, ischemia, risk ofcardiovascular event or disease, CAD-RADS score, and/or progression ortracking of any of the foregoing. In some embodiments, thepatient-specific report can include a proposed treatment, such asstatins, lifestyle changes, and/or surgery.

In some embodiments, the system can be configured to access and/orretrieve from a patient report database 500 one or more phrases,characterizations, graphics, videos, audio files, and/or the like thatare applicable and/or can be used to generate the patient-specificreport. In generating the patient-specific report, in some embodiments,the system can be configured to compare one or more parameters, such asthose mentioned above and/or derived from a medical image of thepatient, with one or more parameters previously derived from otherpatients. For example, in some embodiments, the system can be configuredto compare one or more quantified plaque parameters derived from themedical image of the patient with one or more quantified plaqueparameters derived from medical images of other patients in the similaror same age group. Based on the comparison, in some embodiments, thesystem can be configured to determine which phrases, characterizations,graphics, videos, audio files, and/or the like to include in thepatient-specific report, for example by identifying similar previouscases. In some embodiments, the system can be configured to utilize anAI and/or ML algorithm to generate the patient-specific report. In someembodiments, the patient-specific report can include a document, ARexperience, VR experience, video, and/or audio component.

FIGS. 5B-5I illustrate example embodiment(s) of a patient-specificmedical report generated based on medical image analysis. In particular,FIG. 5B illustrates an example cover page of a patient-specific report.

FIGS. 5C-5I illustrate portions of an example patient-specificreport(s). In some embodiments, a patient-specific report generated bythe system may include only some or all of these illustrated portions.As illustrated in FIGS. 5C-5I, in some embodiments, the patient-specificreport includes a visualization of one or more arteries and/or portionsthereof, such as for example, the Right Coronary Artery (RCA),R-Posterior Descending Artery (R-PDA), R-Posterolateral Branch (R-PLB),Left Main (LM) and Left Anterior Descending (LAD) Artery, 1st Diagonal(D1) Artery, 2nd Diagonal (D2) Artery, Circumflex (Cx) Artery, 1stObtuse Marginal Branch (OM1), 2nd Obtuse Marginal Branch (OM2), RamusIntermedius (RI), and/or the like. In some embodiments, for each of thearteries included in the report, the system is configured to generate astraightened view for easy tracking along the length of the vessel, suchas for example at the proximal, mid, and/or distal portions of anartery.

In some embodiments, a patient-specific report generated by the systemincludes a quantified measure of various plaque and/or vascularmorphology-related parameters shown within the vessel. In someembodiments, for each or some of the arteries included in the report,the system is configured to generate and/or derive from a medical imageof the patient and include in a patient-specific report a quantifiedmeasure of the total plaque volume, total low-density or non-calcifiedplaque volume, total non-calcified plaque value, and/or total calcifiedplaque volume. Further, in some embodiments, for each or some of thearteries included in the report, the system is configured to generateand/or derive from a medical image of the patient and include in apatient-specific report a quantified measure of stenosis severity, suchas for example a percentage of the greatest diameter stenosis within theartery. In some embodiments, for each or some of the arteries includedin the patient-specific report, the system is configured to generateand/or derive from a medical image of the patient and include in apatient-specific report a quantified measure of vascular remodeling,such as for example the highest remodeling index.

Visualization / GUI

Atherosclerosis, the buildup of fats, cholesterol and other substancesin and on your artery walls (e.g., plaque), which can restrict bloodflow. The plaque can burst, triggering a blood clot. Althoughatherosclerosis is often considered a heart problem, it can affectarteries anywhere in the body. However, determining information aboutplaque in coronary arteries can be difficult due in part to imperfectimaging data, aberrations that can be present in coronary artery images(e.g., due to movement of the patient), and differences in themanifestation of plaque in different patients. Accordingly, neithercalculated information derived from CT images, or visual inspection ofthe CT images, alone provide sufficient information to determineconditions that exist in the patient’s coronary arteries. Portions ofthis disclosure describe information they can be determined from CTimages using automatic or semiautomatic processes. For example, using amachine learning process has been trained on thousands of CT scansdetermine information depicted in the CT images, and/or utilizinganalyst to review and enhance the results of the machine learningprocess, and the example user interfaces described herein can providethe determined information to another analyst or a medical practitioner.While the information determined from the CT images is invaluable inassessing the condition of a patient’s coronary arteries, visualanalysis of the coronary arteries by skilled medical practitioner, withthe information determined from the CT images in-hand, allows a morecomprehensive assessment of the patient’s coronary arteries. Asindicated herein, embodiments of the system facilitate the analysis andvisualization of vessel lumens, vessel walls, plaque and stenosis in andaround coronary vessels. This system can display vessels in multi-planarformats, cross-sectional views, 3D coronary artery tree view, axial,sagittal, and coronal views based on a set of computerized tomography(CT) images, e.g., generated by a CT scan of a patient’s vessels. The CTimages can be Digital Imaging and Communications in Medicine (DICOM)images, a standard for the communication and management of medicalimaging information and related data. CT images, or CT scans, as usedherein, is a broad term that refers to pictures of structures within thebody created by computer controlled scanner. For example, by a scannerthat uses an X-ray beam. However, it is appreciated that other radiationsources and/or imaging systems may produce a set of CT-like images.Accordingly, the use of the term “CT images” herein may refer to anytype of imaging system having any type of imaging source that produces aset of images depicting “slices” of structures within a body, unlessotherwise indicated. One key aspect of the user interface describedherein is the precise correlation of the views and information that isdisplayed of the CT images. Locations in the CT images displayed onportions (or “panels”) of the user interface are correlated precisely bythe system such that the same locations are displayed concurrently in adifferent views. By simultaneously displaying a portion of the coronaryvessel in, for example, two, three, four, five or six viewssimultaneously, and allowing a practitioner to explore particularlocations of a coronary vessel in one view while the other 2-6 viewscorrespondingly show the exact same location provides an enormous amountof insight into the condition of the vessel and allows thepractitioner/analyst to quickly and easily visually integrate thepresented information to gain a comprehensive and accurate understandingof the condition of the coronary vessel being examined.

Advantageously, the present disclosure allows CT images and data to beanalyzed in a more useful and accurate way, for users to interact andanalyze images and data in a more analytically useful way and/or forcomputation analysis to be performed in a more useful way, for exampleto detect conditions requiring attention. The graphical user interfacesin the processing described herein allow a user to visualize otherwisedifficult to define relationships between different information andviews of coronary arteries. In an example, displaying a portion of acoronary artery simultaneously in a CMPR view, a SMPR view, and across-sectional view can provide insight to an analyst of plaque orstenosis associated with the coronary artery that may not otherwise beperceivable using a fewer number of views. Similarly, displaying theportion of the coronary artery in an axial view, a sagittal view, and acoronal view, in addition to the CMPR view, the SMPR view, and thecross-sectional view can provide further information to the analyst thatwould not otherwise be perceivable with a fewer number of views of thecoronary artery. In various embodiments, any of the informationdescribed or illustrated herein, determined by the system or an analystinteracting with the system, and other information (for example, fromanother outside source, e.g., an analyst) that relates to coronaryarteries/vessels associated with the set of CT images (“arteryinformation”) including information indicative of stenosis and plaque ofsegments of the coronary vessels in the set of CT images, andinformation indicative of identification and location of the coronaryvessels in the set of CT images, can be stored on the system andpresented in various panels of the user interface and in reports. Thepresent disclosure allows for easier and quicker analysis of a patient’scoronary arteries and features associate with coronary arteries. Thepresent disclosure also allows faster analysis of coronary artery databy allowing quick and accurate access to selected portions of coronaryartery data. Without using the present system and methods of thedisclosure, quickly selecting, displaying, and analyzing CT images andcoronary artery information, can be cumbersome and inefficient, and maylead to analyst missing critical information in their analysis of apatient’s coronary arteries, which may lead to inaccurate evaluation ofa patient’s condition.

In various embodiments, the system can identify a patient’s coronaryarteries either automatically (e.g., using a machine learning algorithmduring the preprocessing step of set of CT images associated with apatient), or interactively (e.g., by receiving at least some input forma user) by an analyst or practitioner using the system. As describedherein, in some embodiments, the processing of the raw CT scan data cancomprise analysis of the CT data in order to determine and/or identifythe existence and/or nonexistence of certain artery vessels in apatient. As a natural occurring phenomenon, certain arteries may bepresent in certain patients whereas such certain arteries may not existin other patients. In some embodiments, the system can be configured toidentify and label the artery vessels detected in the scan data. Incertain embodiments, the system can be configured to allow a user toclick upon a label of an identified artery within the patient, andthereby allowing that artery to be highlighted in an electronicrepresentation of a plurality of artery vessels existing in the patient.In some embodiments, the system is configured to analyze arteriespresent in the CT scan data and display various views of the arteriespresent in the patient, for example within 10-15 minutes or less. Incontrast, as an example, conducting a visual assessment of a CT toidentify stenosis alone, without consideration of good or bad plaque orany other factor, can take anywhere between 15 minutes to more than anhour depending on the skill level, and can also have substantialvariability across radiologists and/or cardiac imagers.

Although some systems may allow an analyst to view the CT imagesassociated with a patient, they lack the ability to display all of thenecessary views, in real or near real-time, with correspondence between3-D artery tree views of coronary arteries specific to a patient,multiple SMPR views, and a cross-sectional, as well as an axial view, asagittal view, and/or the coronal view. Embodiments of the system can beconfigured this display one or more of the use, or all of the use, whichprovides unparalleled visibility of a patient’s coronary arteries, andallows an analyst or practitioner to perceive features and informationthat is simply may not be perceivable without these views. That is, auser interface configured to show all of these views, as well asinformation related to the displayed coronary vessel, allows an analystor practitioner to use their own experience in conjunction with theinformation that the system is providing, to better identify conditionsof the arteries which can help them make a determination on treatmentsfor the patient. In addition, the information that is determined by thesystem and displayed by the user interface that cannot be perceived byan analyst or practitioner is presented in such a manner that is easy tounderstand and quick to assimilate. As an example, the knowledge ofactual radiodensity values of plaque is not something that analyst anddetermine simply by looking at the CT image, but the system can andpresent a full analysis of all plaque is found.

In general, arteries vessels are curvilinear in nature. Accordingly, thesystem can be configured to straighten out such curvilinear arteryvessels into a substantially straight-line view of the artery, and insome embodiments, the foregoing is referred to as a straight multiplanarreformation (MPR) view. In some embodiments, the system is configured toshow a dashboard view with a plurality of artery vessels showing in astraight multiplanar reformation view. In some embodiments, the linearview of the artery vessels shows a cross-sectional view along alongitudinal axis (or the length of the vessel or a long axis) of theartery vessel. In some embodiments, the system can be configured toallow the user to rotate in a 360° fashion about the longitudinal axisof the substantially linear artery vessels in order for the user toreview the vessel walls from various views and angles. In someembodiments, the system is configured to not only show the narrowing ofthe inner vessel diameter but also characteristics of the inner and/orouter vessel wall itself. In some embodiments, the system can beconfigured to display the plurality of artery vessels in a multiplelinear views, e.g., in an SMPR view.

In some embodiments, the system can be configured to display theplurality of artery vessels in a perspective view in order to bettershow the user the curvatures of the artery vessels. In some embodiments,the perspective view is referred to as a curved multiplanar reformationview. In some embodiments, the perspective view comprises the CT imageof the heart and the vessels, for example, in an artery tree view. Insome embodiments, the perspective view comprises a modified CT imageshowing the artery vessels without the heart tissue displayed in orderto better highlight the vessels of the heart. In some embodiments, thesystem can be configured to allow the user to rotate the perspectiveview in order to display the various arteries of the patient fromdifferent perspectives. In some embodiments, the system can beconfigured to show a cross-sectional view of an artery vessel along alatitudinal axis (or the width of the vessel or short axis). In contrastto the cross-sectional view along a longitudinal axis, in someembodiments, the system can allow a user to more clearly see thestenosis or vessel wall narrowing by viewing the artery vessel from across-sectional view across a latitudinal axis.

In some embodiments, the system is configured to display the pluralityof artery vessels in an illustrative view or cartoon view. In theillustrative view of the artery vessels, in some embodiments, the systemcan utilize solid coloring or grey scaling of the specific arteryvessels or sections of specific artery vessels to indicate varyingdegrees of risk for a cardiovascular event to occur in a particularartery vessel or section of artery vessel. For example, the system canbe configured to display a first artery vessel in yellow to indicate amedium risk of a cardiovascular event occurring in the first arteryvessel while displaying a second artery vessel in red to indicate a highrisk of a cardiovascular event occurring in the second artery vessel. Insome embodiments, the system can be configured to allow the user tointeract with the various artery vessels and/or sections of arteryvessels in order to better understand the designated risk associatedwith the artery vessel or section of artery vessel. In some embodiments,the system can allow the user to switch from the illustrative view to aCT view of the arteries of the patient.

In some embodiments, the system can be configured to display in a singledashboard view all or some of the various views described herein. Forexample, the system can be configured to display the linear view withthe perspective view. In another example, the system can be configuredto display the linear view with the illustrative view.

In some embodiments, the processed CT image data can result in allowingthe system to utilize such processed data to display to a user variousarteries of a patient. As described above, the system can be configuredto utilize the processed CT data in order to generate a linear view ofthe plurality of artery vessels of a patient. In some embodiments, thelinear view displays the arteries of a patient as in a linear fashion toresemble a substantially straight line. In some embodiments, thegenerating of the linear view requires the stretching of the image ofone or more naturally occurring curvilinear artery vessels. In someembodiments, the system can be configured to utilize such processed datato allow a user to rotate a displayed linear view of an artery in a 360°rotatable fashion. In some embodiments, the processed CT image data canvisualize and compare the artery morphologies over time, i.e.,throughout the cardiac cycle. The dilation of the arteries, or lackthereof, may represent a healthy versus sick artery that is not capableof vasodilation. In some embodiments, a prediction algorithm can be madeto determine the ability of the artery to dilate or not, by simplyexamining a single point in time.

As mentioned above, aspects of the system can help to visualize apatient’s coronary arteries. In some embodiments, the system can beconfigured to utilize the processed data from the raw CT scans in orderto dynamically generate a visualization interface for a user to interactwith and/or analyze the data for a particular patient. The visualizationsystem can display multiple arteries associated with a patient’s heart.The system can be configured to display multiple arteries in asubstantially linear fashion even though the arteries are not linearwithin the body of the patient. In some embodiments, the system can beconfigured to allow the user to scroll up and down or left to rightalong the length of the artery in order to visualize different areas ofthe artery. In some embodiments, the system can be configured to allow auser to rotate in a 360° fashion an artery in order to allow the user tosee different portions of the artery at different angles.

Advantageously, the system can be configured to comprise or generatemarkings in areas where there is an amount of plaque buildup thatexceeds a threshold level. In some embodiments, the system can beconfigured to allow the user to target a particular area of the arteryfor further examination. The system can be configured to allow the userto click on one or more marked areas of the artery in order to displaythe underlying data associated with the artery at a particular pointalong the length of the artery. In some embodiments, the system can beconfigured to generate a cartoon rendition of the patient’s arteries. Insome embodiments, the cartoon or computer-generated representation ofthe arteries can comprise a color-coded scheme for highlighting certainareas of the patient’s arteries for the user to examine further. In someembodiments, the system can be configured to generate a cartoon orcomputer-generated image of the arteries using a red color, or any othergraphical representation, to signify arteries that require furtheranalysis by the user. In some embodiments, the system can label thecartoon representation of the arteries, and the 3D representation of thearteries described above, with stored coronary vessel labels accordingto the labeling scheme. If a user desires, the labeling scheme can bechanged or refined and preferred labels may be stored and used labelcoronary arteries.

In some embodiments, the system can be configured to identify areas inthe artery where ischemia is likely to be found. In some embodiments,the system can be configured to identify the areas of plaque in whichbad plaque exists. In some embodiments, the system can be configured toidentify bad plaque areas by determining whether the coloration and/orthe gray scale level of the area within the artery exceeds a thresholdlevel. In an example, the system can be configured to identify areas ofplaque where the image of a plaque area is black or substantially blackor dark gray. In an example, the system can be configured to identifyareas of “good” plaque by the designation of whiteness or light grey ina plaque area within the artery.

In some embodiments, the system is configured to identify portions of anartery vessel where there is high risk for a cardiac event and/or drawan outline following the vessel wall or profiles of plaque build-upalong the vessel wall. In some embodiments, the system is furtherconfigured to display this information to a user and/or provide editingtools for the user to change the identified portions or the outlinedesignations if the user thinks that the AI algorithm incorrectly drewthe outline designations. In some embodiments, the system comprises anediting tool referred to as “snap-to-lumen,” wherein the user selects aregion of interest by drawing a box around a particular area of thevessel and selecting the snap-to-lumen option and the systemautomatically redraws the outline designation to more closely track theboundaries of the vessel wall and/or the plaque build-up, wherein thesystem is using image processing techniques, such as but not limited toedge detection. In some embodiments, the AI algorithm does not processthe medical image data with complete accuracy and therefore editingtools are necessary to complete the analysis of the medical image data.In some embodiments, the final user editing of the medical image dataallows for faster processing of the medical image data than using solelyAI algorithms to process the medical image data.

In some embodiments, the system is configured to replicate images fromhigher resolution imaging. As an example, in CT, partial volumeartifacts from calcium are a known artifact of CT that results inoverestimation of the volume of calcium and the narrowing of an artery.By training and validating a CT artery appearance to that ofintravascular ultrasound or optical coherence tomography orhistopathology, in some embodiments, the CT artery appearance may bereplicated to be similar to that of IVUS or OCT and, in this way,debloom the coronary calcium artifacts to improve the accuracy of the CTimage.

In some embodiments, the system is configured to provide a graphicaluser interface for displaying a vessel from a beginning portion to anending portion and/or the tapering of the vessel over the course of thevessel length. Many examples of panels that can be displayed in agraphical user interface are illustrated and described in reference toFIGS. 6A-9N. In some embodiments, portions of the user interface,panels, buttons, or information displayed on the user interface bearranged differently than what is described herein and illustrated inthe Figures. For example, a user may have a preference for arrangingdifferent views of the arteries in different portions of the userinterface.

In some embodiments, the graphical user interface is configured toannotate the displayed vessel view with plaque build-up data obtainedfrom the AI algorithm analysis in order to show the stenosis of thevessel or a stenosis view. In some embodiments, the graphical userinterface system is configured to annotate the displayed vessel viewwith colored markings or other markings to show areas of high risk orfurther analysis, areas of medium risk, and/or areas of low risk. Forexample, the graphical user interface system can be configured toannotate certain areas along the vessel length in red markings, or othergraphical marking, to indicate that there is significant bad fattyplaque build-up and/or stenosis. In some embodiments, the annotatedmarkings along the vessel length are based on one or more variable suchas but not limited to stenosis, biochemistry tests, biomarker tests, AIalgorithm analysis of the medical image data, and/or the like. In someembodiments, the graphical user interface system is configured toannotate the vessel view with an atherosclerosis view. In someembodiments, the graphical user interface system is configured toannotate the vessel view with an ischemia view. In some embodiments, thegraphical user interface is configured to allow the user to rotate thevessel 180 degrees or 360 degrees in order to display the vessel and theannotated plaque build-up views from different angles. From this view,the user can manually determine the stent length and diameter foraddressing the stenosis, and in some embodiments, the system isconfigured to analyze the medical image information to determine therecommended stent length and diameter, and display the proposed stentfor implantation in the graphical user interface to illustrate to theuser how the stent would address the stenosis within the identified areaof the vessel. In some embodiments, the systems, methods, and devicesdisclosed herein can be applied to other areas of the body and/or othervessels and/or organs of a subject, whether the subject is human orother mammal.

Illustrative Example

One of the main uses of such systems can be to determine the presence ofplaque in vessels, for example but not limited to coronary vessels.Plaque type can be visualized based on Hounsfield Unit density forenhanced readability for the user. Embodiments of the system alsoprovide quantification of variables related to stenosis and plaquecomposition at both the vessel and lesion levels for the segmentedcoronary artery.

In some embodiments, the system is configured as a web-based softwareapplication that is intended to be used by trained medical professionalsas an interactive tool for viewing and analyzing cardiac CT data fordetermining the presence and extent of coronary plaques (i.e.,atherosclerosis) and stenosis in patients who underwent CoronaryComputed Tomography Angiography (CCTA) for evaluation of coronary arterydisease (CAD), or suspected CAD. This system post processes CT imagesobtained using a CT scanner. The system is configured to generate a userinterface that provides tools and functionality for thecharacterization, measurement, and visualization of features of thecoronary arteries.

Features of embodiments of the system can include, for example,centerline and lumen/vessel extraction, plaque composition overlay, useridentification of stenosis, vessel statistics calculated in real time,including vessel length, lesion length, vessel volume, lumen volume,plaque volume (non-calcified, calcified, low-density-non-calcifiedplaque and total), maximum remodeling index, and area/diameter stenosis(e.g., a percentage), two dimensional (2D) visualization of multi-planarreformatted vessel and cross-sectional views, interactive threedimensional (3D) rendered coronary artery tree, visualization of acartoon artery tree that corresponds to actual vessels that appear inthe CT images, semi-automatic vessel segmentation that is usermodifiable, and user identification of stents and Chronic TotalOcclusion (CTO).

In an embodiment, the system uses 18 coronary segments within thecoronary vascular tree (e.g., in accordance with the guidelines of theSociety of Cardiovascular Computed Tomography). The coronary segmentlabels include:

-   pRCA - proximal right coronary artery-   mRCA - mid right coronary artery-   dRCA - distal right coronary artery-   R-PDA - right posterior descending artery-   LM - left main artery-   pLAD - proximal left anterior descending artery-   mLAD - mid left anterior descending artery-   dLAD - distal left anterior descending artery-   D1 - first diagonal-   D2 - second diagonal-   pCx - proximal left circumflex artery-   OM1 - first obtuse marginal-   LCx - distal left circumflex-   OM2 - second obtuse marginal-   L-PDA - left posterior descending artery-   R-PLB - right posterior lateral branch-   RI - ramus intermedius artery-   L-PLB - left posterior lateral branch

Other embodiments can include more, or fewer, coronary segment labels.The coronary segments present in an individual patient are dependent onwhether they are right or left coronary dominant. Some segments are onlypresent when there is right coronary dominance, and some only when thereis a left coronary dominance. Therefore, in many, if not all instances,no single patient may have all 18 segments. The system will account formost known variants.

In one example of performance of the system, CT scans were processed bythe system, and the resulting data was compared to ground truth resultsproduced by expert readers. Pearson Correlation Coefficients andBland-Altman Agreements between the systems results and the expertreader results is shown in the table below:

Output Pearson Correlation Bland-Altman Agreement Lumen Volume 0.91 96%Vessel Volume 0.93 97% Total Plaque Volume 0.85 95% Calcified PlaqueVolume 0.94 95% Non-Calcified Plaque Volume 0.74 95%Low-Density-Non-Calcified Plaque Volume 0.53 97%

FIGS. 6A - 9N illustrate an embodiment of the user interface of thesystem, and show examples of panels, graphics, tools, representations ofCT images, and characteristics, structure, and statistics related tocoronary vessels found in a set of CT images. In various embodiments,the user interface is flexible and that it can be configured to showvarious arrangements of the panels, images, graphics representations ofCT images, and characteristics, structure, and statistics. For example,based on an analyst’s preference. The system has multiple menus andnavigational tools to assist in visualizing the coronary arteries.Keyboard and mouse shortcuts can also be used to navigate through theimages and information associated with a set of CT images for patient.

FIG. 6A illustrates an example of a user interface 600 that can begenerated and displayed on a CT image analysis system described herein,the user interface 600 having multiple panels (views) that can showvarious corresponding views of a patient’s arteries and informationabout the arteries. In an embodiment, the user interface 600 shown inFIG. 6A can be a starting point for analysis of the patient’s coronaryarteries, and is sometimes referred to herein as the “Study Page” (orthe Study Page 600). In some embodiments, the Study Page can include anumber of panels that can be arranged in different positions on the userinterface 600, for example, based on the preference the analyst. Invarious instances of the user interface 600, certain panels of thepossible panels that may be displayed can be selected to be displayed(e.g., based on a user input).

The example of the Study Page 600 shown in FIG. 6A includes a firstpanel 601 (also shown in the circled “2”) including an artery tree 602comprising a three-dimensional (3D) representation of coronary vesselsbased on the CT images and depicting coronary vessels identified in theCT images, and further depicting respective segment labels. Whileprocessing the CT images, the system can determine the extent of thecoronary vessels are determined and the artery tree is generated.Structure that is not part of the coronary vessels (e.g., heart tissueand other tissue around the coronary vessels) are not included in theartery tree 602. Accordingly, the artery tree 602 in FIG. 6A does notinclude any heart tissue between the branches (vessels) 603 of theartery tree 602 allowing visualization of all portions of the arterytree 602 without them being obscured by heart tissue.

This Study Page 600 example also includes a second panel 604 (also shownin the circled “1a”) illustrating at least a portion of the selectedcoronary vessel in at least one straightened multiplanar reformat (SMPR)vessel view. A SMPR view is an elevation view of a vessel at a certainrotational aspect. When multiple SMPR views are displayed in the secondpanel 604 each view can be at a different rotational aspect. Forexample, at any whole degree, or at a half degree, from 0° to 259.5°,where 360° is the same view as 0°. In this example, the second panel 604includes four straightened multiplanar vessels 604 a-d displayed inelevation views at a relative rotation of 0°, 22.5°, 45°, and 67.5°, therotation indicated that the upper portion of the straightenedmultiplanar vessel. In some embodiments, the rotation of each view canbe selected by the user, for example, at the different relative rotationinterval. The user interface includes the rotation tool 605 that isconfigured to receive an input from a user, and can be used to adjustrotation of a SMPR view (e.g., by one or more degrees). One or moregraphics related to the vessel shown in the SMPR view can also bedisplayed. For example, a graphic representing the lumen of the vessel,a graphic representing the vessel wall, and/or a graphic representingplaque.

This Study Page 600 example also includes the third panel 606 (alsoindicated by the circled “1c”), which is configured to show across-sectional view of a vessel 606 a generated based on a CT image inthe set of CT images of the patient. The cross-sectional viewcorresponds to the vessel shown in the SMPR view. The cross-sectionalview also corresponds to a location indicated by a user (e.g., with apointing device) on a vessel in the SMPR view. The user interfacesconfigured such that a selection of a particular location along thecoronary vessel in the second panel 604 displays the associated CT imagein a cross-sectional view in the third panel 606. In this example, agraphic 607 is displayed on the second panel 604 and the third panel 606indicating the extent of plaque in the vessel.

This Study Page 600 example also includes a fourth panel 608 thatincludes anatomical plane views of the selected coronary vessel. In thisembodiment, the Study Page 600 includes an axial plane view 608 a (alsoindicated by the circled “3a”), a coronal plane view 608 b (alsoindicated by the circled “3b”), and a sagittal plane view 608 c (alsoindicated by the circled “3c”). The axial plane view is a transverse or“top” view. The coronal plane view is a front view. The sagittal planeview is a side view. The user interface is configured to displaycorresponding views of the selected coronary vessel. For example, viewsof the selected coronary vessel at a location on the coronary vesselselected by the user (e.g., on one of the SMPR views in the second panel604.

FIG. 6B illustrates another example of the Study Page (user interface)600 that can be generated and displayed on the system, the userinterface 600 having multiple panels that can show various correspondingviews of a patient’s arteries. In this example, the user interface 600displays an 3D artery tree in the first panel 601, the cross-sectionalview in the third panel 606, and axial, coronal, and sagittal planeviews in the fourth panel 608. Instead of the second panel 604 shown inFIG. 6A, the user interface 600 includes a fifth panel 609 showingcurved multiplanar reformat (CMPR) vessel views of a selected coronaryvessel. The fifth panel 609 can be configured to show one or more CMPRviews. In this example, two CMPR views were generated and are displayed,a first CMPR view 609 a at 0° and a second CMPR view 609 b at 90°. TheCMPR views can be generated and displayed at various relative rotations,for example, from 0° to 259.5°. The coronary vessel shown in the CMPRview corresponds to the selected vessel, and corresponds to the vesseldisplayed in the other panels. When a location on the vessel in onepanel is selected (e.g., the CMPR view), the views in the other panels(e.g., the cross-section, axial, sagittal, and coronal views) can beautomatically updated to also show the vessel at that the selectedlocation in the respective views, thus greatly enhancing the informationpresented to a user and increasing the efficiency of the analysis.

FIGS. 6C, 6D, and 6E illustrate certain details of a multiplanarreformat (MPR) vessel view in the second panel, and certainfunctionality associated with this view. After a user verifies theaccuracy of the segmentation of the coronary artery tree in panel 602,they can proceed to interact with the MPR views where edits can be madeto the individual vessel segments (e.g., the vessel walls, the lumen,etc.). In the SMPR and CMPR views, the vessel can be rotated inincrements (e.g., 22.5°) by using the arrow icon 605, illustrated inFIGS. 6C and 6D. Alternatively, the vessel can be rotated continuouslyby 1 degree increments in 360 degrees by using the rotation command 610,as illustrated in FIG. 6E. The vessels can also be rotated by pressingthe COMMAND or CTRL button and left clicking + dragging the mouse on theuser interface 600.

FIG. 6F illustrates additional information of the three-dimensional (3D)rendering of the coronary artery tree 602 on the first panel 601 thatallows a user to view the vessels and modify the labels of a vessel.FIG. 6G illustrates shortcut commands for the coronary artery tree 602,axial view 608 a, sagittal view 608 b, and coronal view 608 c. In panel601 shown in FIG. 6F, a user can rotate the artery tree as well as zoomin and out of the 3D rendering using commands selected in the userinterface illustrated in FIG. 6G. Clicking on a vessel will turn ityellow which indicates that is the vessel that is currently beingreviewed. In this view, users can rename or delete a vessel byright-clicking on the vessel name which opens panel 611, which isconfigured to receive an input from a user to rename the vessel. Panel601 also includes a control that can be activated to turn the displayedlabels “on” or “off.” FIG. 6H further illustrates panel 608 of the userinterface for viewing DICOM images in three anatomical planes: axial,coronal, and sagittal. FIG. 6I illustrates panel 606 showing across-sectional view of a vessel. The scroll, zoom in/out, and pancommands can also be used on these views.

FIGS. 6J and 6K illustrate certain aspects of the toolbar 612 and menunavigation functionality of the user interface 600. FIG. 6J illustratesa toolbar of the user interface for navigating the vessels. The toolbar612 includes a button 612 a, 612 b etc. for each of the vesselsdisplayed on the screen. The user interface 600 is configured to displaythe buttons 612 a-n to indicate various information to the user. In anexample, when a vessel is selected, the corresponding button ishighlighted (e.g., displayed in yellow), for example, button 612 c. Inanother example, a button being dark gray with white lettering indicatesthat a vessel is available for analysis. In an example, a button 612 dthat is shaded black means a vessel could not be analyzed by thesoftware because they are either not anatomically present or there aretoo many artifacts. A button 612 e that is displayed as gray with checkmark indicates that the vessel has been reviewed.

FIG. 6K illustrates a view of the user interface 600 with an expandedmenu to view all the series (of images) that are available for reviewand analysis. If the system has provided more than one of the samevessel segment from different series of images for analysis, the userinterface is configured to receive a user input to selected the desiredseries for analysis. In an example, an input can be received indicatinga series for review by a selection on one of the radio buttons 613 fromthe series of interest. The radio buttons will change from gray topurple when it is selected for review. In an embodiment, the software,by default, selects the two series of highest diagnostic quality foranalysis however, all series are available for review. The user can useclinical judgment to determine if the series selected by the system isof diagnostic quality that is required for the analysis, and shouldselect a different series for analysis if desired. The series selectedby the system is intended to improve workflow by prioritizing diagnosticquality images. The system is not intended to replace the user’s reviewof all series and selection of a diagnostic quality image within astudy. Users can send any series illustrated in FIG. 6K for the systemto suggest vessel segmentations by hovering the mouse over the seriesand select an “Analyze” button 614 as illustrated in FIG. 6L.

FIG. 6M illustrates a panel that can be displayed on the user interface600 to add a new vessel on the image, according to one embodiment. Toadd a new vessel on the image, the user interface 600 can receive a userinput via a “+Add Vessel” button on the toolbar 612. The user interfacewill display a “create Mode” 615 button appear in the fourth panel 608on the axial, coronal and sagittal view. Then the vessel can be added onthe image by scrolling and clicking the left mouse button to createmultiple dots (e.g., green dots). As the new vessel is being added, itwill preview as a new vessel in the MPR, cross-section, and 3D arterytree view. The user interface is configured to receive a “Done” commandto indicate adding the vessel has been completed. Then, to segment thevessels utilizing the system’s semi-automatic segmentation tool, click“Analyze” on the tool bar and the user interface displays suggestedsegmentation for review and modification. The name of the vessel can bechosen by selecting “New” in the 3D artery tree view in the first panel601, which activates the name panel 611 and the name of the vessel canbe selected from panel 611, which then stores the new vessel and itsname. In an embodiment, if the software is unable to identify the vesselwhich has been added by the user, it will return straight vessel linesconnecting the user-added green dots, and the user can adjust thecenterline. The pop-up menu 611 of the user interface allows new vesselsto be identified and named according to a standard format quickly andconsistently.

FIG. 7A illustrates an example of an editing toolbar 714 that includesediting tools which allow users to modify and improve the accuracy ofthe findings resulting from processing CT scans with a machine learningalgorithm, and then processing the CT scans, and information generatedby the machine learning algorithm, by an analyst. In some embodiments,the user interface includes editing tools that can be used to modify andimprove the accuracy of the findings. In some embodiments, the editingtools are located on the lefthand side of the user interface, as shownin FIG. 7A. The following is a listing and description of the availableediting tools. Hovering over each button (icon) will display the name ofeach tool. These tools can be activated and deactivated by clicking onit. If the color of the tool is gray, it is deactivated. If the softwarehas identified any of these characteristics in the vessel, theannotations will already be on the image when the tool is activated. Theediting tools in the toolbar can include one or more of the followingtools: Lumen Wall 701, Snap to Vessel Wall 702, Vessel Wall 703, Snap toLumen Wall 704, Segments 705, Stenosis 706, Plaque Overlay 707,Centerline 708, Chronic Total Occlusion (CTO) 709, Stent 710, Exclude By711, Tracker 712, and Distance 713. The user interface 600 is configuredto activate each of these tools by receiving a user selection on therespective toll icon (shown in the table below and in FIG. 7A) and areconfigured to provide functionality described in the Editing ToolsDescription Table below:=

Editing Tools Description Table

LUMEN WALL USERS CAN ADJUST OR DRAW NEW LUMEN WALL CONTOURS TO IMPROVETHE ACCURACY OF THE LOCATION AND MEASUREMENTS OF THE LUMEN

SNAP TO VESSEL WALL USERS CAN DRAG A SHADED AREA AND RELEASE IT IN ORDERTO SNAP THE LUMEN WALL TO THE VESSEL WALL FOR HEALTHY VESSELS AREAS

VESSEL WALL USERS CAN ADJUST OR DRAW NEW VESSEL WALL CONTOURS TO REFINETHE EXTERIOR OF THE VESSEL WALL

SNAP TO LUMEN WALL USERS CAN DRAG A SHADED AREA AND RELEASE IT IN ORDERTO SNAP THE VESSEL WALL TO THE LUMEN WALL FOR HEALTHY VESSELS AREAS

SEGMENTS USERS CAN ADD SEGMENT MARKERS TO DEIFNE THE BOUNDARIES OF EACHOF THE 18 CORONARY SEGMENTS, NEW OR ALREADY EXISTING MARKERS CAN BEDRAGGED UP AND DOWN TO ADJUST TO THE EXACT SEGMENT BOUNDARIES.

STENOSIS THIS TOOL CONSISTS OF 5 MARKERS THAT ALLOW USERS TO MARKREGIONS OF STENOSIS ON THE VESSEL. USERS CAN ADD NEW STENOSIS MARKERSAND NEW OR ALREADY EXISTING MARKERS CAN BE DRAGGED UP/DOWN.

PLAQUE OVERLAY THIS TOOL OVERLAYS THE SMPR AND THE CROSS SECTION VIEWS.WITH COLORIZED AREAS OF PLAQUE BASED UPON THE PLAQUES HOUNS FIELDATTENUATION

CENTERLINE USERS CAN ADJUST THE CENTER LINE OF THE VESSEL IN THE CMPR ORCROSS-SECTION VIEW ADJUSTMENTS WILL BE PROPAGATED TO THE SMPR VIEW.

CTO CHRONIC TOTAL OCCLUSION TOOL CONSIST OF TWO MARKERS THAT IDENTIFYTHE START AND END OF A SECTION OF AN ARTERY THAT IS TOTALLY OCCLUDED.MULTIPLE CTOS CAN BE ADDED AND DRAGGED TO THE AREA OF INTEREST.

STENT THE STENT TOOL ALLOW USERS TO IDENTIFY THE PRESENCE OF STENT(S) INTHE CORONARY ARTERIES. USERS CAN ADD STENT MARKERS AND DRAG EXISTINGMARKERS UP OR DOWN TO THE EXACT STENT BOUNDARIES.

EXCLUDE BY USING THIS TOOL, SECTIONS OF A VESSEL CAN BE REMVED FROM THEFINAL CALCULATIONS ANALYSIS. REMOVAL OF THESE SECTIONS IS OFTEN DUE TOTHE PRESENCE OF ARTFACTS. USUALLY DUE TO MOTION OR MISALIGHNMENT ISSUESAMONG OTHERS.

TRACKER THE TRACKER ORIENTS AND ALLOWS USERS TO CORRELATE THE MPR,CROSS-SECTION, AXIAL, CORONAL, SAGITTAL, AND 3D ARTERY TREE VEWS.

DISTANCE THE TOOL IS USED ON THE MPR, CROSS-SECTION AXIAL, CORANAL, ORSAGITTAL VIEWS TO MEASURE DISTANCES BETWEEN POINTS THE TOOL PROVIDESACCURATE READINGS IN MILLIMETERS ALLOWING FOR QUICK REVIEW ANDESTIMATION ON AREAS OF INEREST.

FIGS. 7B and 7C illustrate certain functionality of the Tracker tool.The Tracker tool 712 orients and allows user to correlate the viewsshown in the various panels of the user interface 600, for example, inthe SMPR, CMPR, cross-section, axial, coronal, sagittal, and the 3Dartery tree views. To activate, the tracker icon is selected on theediting toolbar. When the Tracker tool 712 is activated, the userinterface generates and displays a line 616 (e.g., a red line) on theSMPR or CMPR view. The system generates on the user interface acorresponding (red) disc 617 which is displayed on the 3D artery tree inthe first panel 601 in a corresponding location as the line 616. Thesystem generates on the user interface a corresponding (red) dot whichhis displayed on the axial, sagittal and coronal views in the fourthpanel 608 in a corresponding location as the line 616. The line 616,disc 617, and dots 618 are location indicators all referencing the samelocation in the different views, such that scrolling any of the trackersup and down will also result in the same movement of the locationindicator in other views. Also, the user interface 600 displays thecross-sectional image in panel 606 corresponding to the locationindicated by the location indicators.

FIGS. 7D and 7E illustrate certain functionality of the vessel and lumenwall tools, which are used to modify the lumen and vessel wall contours.The Lumen Wall tool 701 and the Vessel Wall tool 703 are configured tomodify the lumen and vessel walls (also referred to herein as contours,boundaries, or features) that were previously determined for a vessel(e.g., determined by processing the CT images using a machine learningprocess. These tool are used by the system for determining measurementsthat are output or displayed. By interacting with the contours generatedby the system with these tools, a user can refine the accuracy of thelocation of the contours, and any measurements that are derived fromthose contours. These tools can be used in the SMPR and cross-sectionview. The tools are activated by selecting the vessel and lumen icons701, 703 on the editing toolbar. The vessel wall 619 will be displayedin the MPR view and the cross-section view in a graphical “trace”overlay in a color (e.g., yellow). The lumen wall 629 will be displayedin a graphical “trace” overly in a different color (e.g., purple). In anembodiment, the user interface is configured to refine the contoursthrough interactions with a user. For example, to refine the contours,the user can hover above the contour with a pointing device (e.g.,mouse, stylus, finger) so it highlights the contour, click on thecontour for the desired vessel or lumen wall and drag the displayedtrace to a different location setting a new boundary. The user interface600 is configured to automatically save any changes to these tracings.The system re-calculates any measurements derived from the changescontours in real time, or near real time. Also, the changes made in onepanel on one view are displayed correspondingly in the other views /panels.

FIG. 7F illustrates the lumen wall button 701 and the snap to vesselwall button 702 (left) and the vessel wall button 703 and the snap tolumen wall button 704 (right) of the user interface 600 which can beused to activate the Lumen Wall/Snap to Vessel tools 701, 702, and theVessel Wall/Snap to Lumen Wall 703, 704 tools, respectively. The userinterface provides these tools to modify lumen and vessel wall contoursthat were previously determined. The Snap to Vessel/Lumen Wall tools areused to easily and quickly close the gap between lumen and vessel wallcontours, that is, move a trace of the lumen contour and a trace of thevessel contour to be the same, or substantially the same, savinginteractive editing time. The user interface 600 is configured toactivate these tools when a user hovers of the tools with a pointingdevice, which reveals the snap to buttons. For example, hovering overthe Lumen Wall button 701 reveals the Snap to Vessel button 702 to theright-side of the Lumen wall button, and hovering over the Vessel Wallbutton 703 reveals the Snap to Lumen Wall button 704 beside the VesselWall button 703. A button is selected to activate the desired tool. Inreference to FIG. 7G, a pointing device can be used to click at a firstpoint 620 and drag along the intended part of the vessel to edit to asecond point 621, and an area 622 will appear indicating where the toolwill run. Once the end of the desired area 622 is drawn, releasing theselection will snap the lumen and vessel walls together.

FIG. 7H illustrates an example of the second panel 602 that can bedisplayed while using the Segment tool 705 which allows for marking theboundaries between individual coronary segments on the MPR. The userinterface 600 is configured such that when the Segment tool 705 isselected, lines (e.g., lines 623, 624) appear on the vessel image in thesecond panel 602 on the vessels in the SMPR view. The lines indicatesegment boundaries that were determined by the system. The names aredisplayed in icons 625, 626 adjacent to the respective line 623, 624. Toedit the name of the segment, click on an icon 625, 626 and labelappropriately using the name panel 611, illustrated in FIG. 7I. Asegment can also be deleted, for example, by selecting a trashcan icon.The lines 623, 624 can be moved up and down to define the segment ofinterest. If a segment is missing, the user can add a new segment usinga segment addition button, and labeled using the labeling feature in thesegment labeling pop-up menu 611.

FIGS. 7J - 7M illustrate an example of using the stenosis tool 706 onthe user interface 600. For example, FIG. 7L illustrates a stenosisbutton which can be used to drop stenosis markers based on the useredited lumen and vessel wall contours. FIG. 7M illustrates the stenosismarkers on segments on a curved multiplanar vessel (CMPR) view. Thesecond panel 604 can be displayed while using the stenosis tool 706which allows a user to indicate markers to mark areas of stenosis on avessel. In an embodiment, the stenosis tool contains a set of fivemarkers that are used to mark areas of stenosis on the vessel. Thesemarkers are defined as:

-   R1: Nearest proximal normal slice to the stenosis/lesion-   P: Most proximal abnormal slice of the stenosis/lesion-   O: Slice with the maximum occlusion-   D: Most distal abnormal slice of the stenosis/lesion-   R2: Nearest distal normal slice to the stenosis/lesion

In an embodiment, there are two ways to add stenosis markers to themultiplanar view (straightened and curved). After selecting the stenosistool 706, a stenosis can be added by activating the stenosis buttonshown in FIG. 7K or FIG. 7L: to drop 5 evenly spaced stenosis markers(i) click on the Stenosis “+” button (FIG. 7K); (ii) a series of 5evenly spaced yellow lines will appear on the vessel; the user must editthese markers to the applicable position; (iii) move all 5 markers atthe same time by clicking inside the highlighted area encompassed by themarkers and dragging them up/down; (iv) move the individual markers byclicking on the individual yellow lines or tags and move up and down;(v) to delete a stenosis, click on the red trashcan icon. To dropstenosis markers based on the user-edited lumen and vessel wallcontours, click on the stenosis button (see FIG. 7L). A series of 5yellow lines will appear on the vessel. The positions are based on theuser-edited contours. The user interface 600 provides functionality fora user to edit the stenosis markers, e.g., can move the stenosis markersFIG. 7J illustrates the stenosis markers R1, P, O, D, and R2 placed onvessels in a SMPR view. FIG. 7M illustrates the markers R1, P, O, D, andR2 placed on vessels in a CMPR view.

FIG. 7N illustrates an example of a panel that can be displayed whileusing the Plaque Overlay tool 707 of the user interface. In anembodiment and in reference to FIG. 7N, “Plaque” is categorized as:low-density-non-calcified plaque (LD-NCP) 701, non-calcified plaque(NCP) 632, or calcified plaque (CP) 633. Selecting the Plaque Overlaytool 707 on the editing toolbar activates the tool. When activated, thePlaque Overlay tool 707 overlays different colors on vessels in the SMPRview in the second panel 604, and in the cross-section the SMPR, andcross-section view in the third panel 606 (see for example, FIG. 7R)with areas of plaque based on Hounsfield Unit (HU) density. In addition,a legend opens in the cross-section view corresponding to plaque type toplaque overlay color as illustrated in FIGS. 7O and 7Q. Users can selectdifferent HU ranges for the three different types of plaque by clickingon the “Edit Thresholds” button located in the top right corner of thecross-section view as illustrated in FIG. 7P. In one embodiment, plaquethresholds default to the values shown in the table below:

Plaque Type Hounsfield Unit (HU) LD-NCO -189 to 30 NCP -189 to 350 CP350 to 2500

The default values can be revised, if desired, for example, using thePlaque Threshold interface shown in FIG. 7Q. Although default values areprovided, users can select different plaque thresholds based on theirclinical judgment. Users can use the cross-section view of the thirdpanel 606, illustrated in FIG. 7R, to further examine areas of interest.Users can also view the selected plaque thresholds in a vesselstatistics panel of the user interface 600, illustrated in FIG. 7S.

The Centerline tool 708 allows users to adjust the center of the lumen.Changing a center point (of the centerline) may change the lumen andvessel wall and the plaque quantification, if present. The Centerlinetool 708 is activated by selecting it on the user interface 600. A line635 (e.g., a yellow line) will appear on the CMPR view 609 and a point634 (e.g., a yellow point) will appear in the cross-section view on thethird panel 606. The centerline can be adjusted as necessary by clickingand dragging the line/point. Any changes made in the CMPR view will bereflected in the cross-section view, and vice-versa. The user interface600 provides for several ways to extend the centerline of an existingvessel. For example, a user can extend the centerline by: (1)right-clicking on the dot 634 delineated vessel on the axial, coronal,or sagittal view (see FIG. 7U); (2) select “Extend from Start” or“Extend from End” (see FIG. 7U), the view will jump to the start or endof the vessel; (3) add (green) dots to extend the vessel (see FIG. 7V);(4) when finished, select the (blue) check mark button, to cancel theextension, select the (red) “x” button (see for example, FIG. 7V). Theuser interface then extends the vessel according to the changes made bythe user. A user can then manually edit the lumen and vessel walls onthe SMPR or cross-section views (see for example, FIG. 7W). If the userinterface is unable to identify the vessel section which has been addedby the user, it will return straight vessel lines connecting theuser-added dots. The user can then adjust the centerline.

The user interface 600 also provides a Chronic Total Occlusion (CTO)tool 709 to identify portions of an artery with a chronic totalocclusion (CTO), that is, a portion of artery with 100% stenosis and nodetectable blood flow. Since it is likely to contain a large amount ofthrombus, the plaque within the CTO is not included in overall plaquequantification. To activate, click on the CTO tool 709 on the editingtoolbar 612. To add a CTO, click on the CTO “+” button on the userinterface. Two lines (markers) 636, 637 will appear on the MPR view inthe second panel 604, as illustrated in FIG. 7X indicating a portion ofthe vessel of the CTO. The markers 636, 637 can be moved to adjust theextent of the CTO. If more than one CTO is present, additional CTO’s canbe added by again activating the CTO “+” button on the user interface. ACTO can also be deleted, if necessary. The location of the CTO isstored. In addition, portions of the vessel that are within thedesignated CTO are not included in the overall plaque calculation, andthe plaque quantification determination is re-calculated as necessaryafter CTO’s are identified.

The user interface 600 also provides a Stent tool 710 to indicate wherein vessel a stent exists. The Stent tool is activated by a userselection of the Stent tool 710 on the toolbar 612. To add a stent,click on the Stent “+” button provided on the user interface. Two lines638, 639 (e.g., purple lines) will appear on of the MPR view asillustrated in FIG. 7Y, and the lines 638, 639 can be moved to indicatethe extend of the stent by clicking on the individual lines 638, 639 andmoving them up and down along the vessel to the ends of the stent.Overlapping with the stent (or the CTO/Exclusion/Stenosis) markers isnot permitted by the user interface 600. A stent can also be deleted.

The user interface 600 also provides an Exclude tool 711 that isconfigured to indicate a portion of a vessel to exclude from theanalysis due to blurring caused by motion, contrast, misalignment, orother reasons. Excluding poor quality images will improve the overallquality of the results of the analysis for the non-excluded portions ofthe vessels. To exclude the top or bottom portion of a vessel, activatethe segment tool 705 and the exclude tool 711 in the editing toolbar612. FIG. 7Z illustrates the use of the exclusion tool to exclude aportion from the top of the vessel. FIG. 7AA illustrates the use of theexclusion tool to exclude a bottom portion of the vessel. A firstsegment marker acts as the exclusion marker for the top portion of thevessel. The area enclosed by exclusion markers is excluded from allvessel statistic calculations. An area can be excluded by dragging thetop segment marker to the bottom of the desired area of exclusion. Theexcluded area will be highlighted. Or the “End” marker can be dragged tothe top of the desired area of exclusion. The excluded area will behighlighted, and a user can enter the reason for an exclusion in theuser interface (see FIG. 7AC). To add a new exclusion to the center ofthe vessel, activate the exclude tool 711 on the editing toolbar 612.Click on the Exclusion “+” button. A pop-up window on the user interfacewill appear for the reason of the exclusion (FIG. 7AC), and the reasoncan be entered and it is stored in reference to the indicated excludedarea. Two markers 640, 641 will appear on the MPR as shown in FIG. 7AB.Move both markers at the same time by clicking inside the highlightedarea. The user can move the individual markers by clicking and draggingthe lines 640, 641. The user interface 600 tracks the locations of theexclusion marker lines 640, 641 (and previously defined features) andprohibits overlap of the area defined by the exclusion lines 640, 641with any previously indicated portions of the vessel having a CTO, stentor stenosis. The user interface 600 also is configured to delete adesignated exclusion.

Now referring to FIG. 7AD-7AG, the user interface 600 also provides aDistance tool 713, which is used to measure the distance between twopoints on an image. It is a drag and drop ruler that captures precisemeasurements. The Distance tool works in the MPR, cross-section, axial,coronal, and sagittal views. To activate, click on the distance tool 713on the editing toolbar 612. Then, click and drag between the desired twopoints. A line 642 and measurement 643 will appear on the imagedisplayed on the user interface 600. Delete the measurement byright-clicking on the distance line 642 or measurement 643 and selecting“Remove the Distance” button 644 on the user interface 600 (see FIG.7AF). FIG. 7AD illustrates an example of measuring a distance of astraightened multiplanar vessel (SMPR). FIG. 7AE illustrates an exampleof measuring the distance 642 of a curved multiplanar vessel (CMPR).FIG. 7AF illustrates an example of measuring a distance 642 of across-section of the vessel. FIG. 7AG illustrates an example ofmeasuring the distance 642 on an Axial View of a patient’s anatomy.

An example of a vessel statistics panel of the user interface 600 isdescribed in reference to FIGS. 7AH - 7AK. FIG. 7AH illustrates a“vessel statistics” portion 645 of the user interface 600 (e.g., abutton) of a panel which can be selected to display the vesselstatistics panel 646 (or “tab”), illustrated in FIG. 7AI. FIG. 7AJillustrates certain functionality on the vessel statistics tab thatallows a user to click through the details of multiple lesions. FIG. 7AKfurther illustrates the vessel panel which the user can use to togglebetween vessels. For example, users can hide the panel by clicking onthe “X” on the top right hand side of the panel, illustrated in FIG.7AI. Statistics are shown at the per-vessel and per-lesion (if present)level, as indicated in FIG. 7AJ.

If more than one lesion is marked by the user, the user can clickthrough each lesion’s details. To view the statistics for each vessel,the users can toggle between vessels on the vessel panel illustrated inFIG. 7AK.

General information pertaining to the length and volume are presentedfor the vessel and lesion (if present) in the vessel statistics panel646, along with the plaque and stenosis information on a per-vessel andper-lesion level. Users may exclude artifacts from the image they do notwant to be considered in the calculations by using the exclusion tool.The following tables indicate certain statistics that are available forvessels, lesions, plaque, and stenosis.

VESSEL Term Definition Vessel Length (mm) Length of a linear coronaryvessel. Total Vessel Volume (mm3) The volume of consecutive slices ofvessel contours. Total Lumen Volume (mm3) The volume of consecutiveslices of lumen contours.

LESION Term Definition Lesion Length (mm) Linear distance from the startof a coronary lesion to the end of a coronary lesion. Vessel Volume(mm3) The volume of consecutive slices of vessel contours. Lumen Volume(mm3) The volume of consecutive slices of lumen contours.

PLAQUE Term Definition Total Calcified Plaque Volume (mm3) Calcifiedplaque is defined as plaque in between the lumen and vessel wall with anattenuation of greater than 350 HU, or as defined by the user, and isreported in absolute measures by plaque volume. Calcified plaques areidentified in each coronary artery≥1.5 mm in mean vessel diameter. TotalNon-Calcified Plaque Volume (mm3) Non-calcified plaque is defined asplaque in between the lumen and vessel wall with an attenuation of lessthan or equal to 350, or as defined by the user, HU and is reported inabsolute measures by plaque volume. The total non-calcified plaquevolume is the sum total of all non-calcified plaques identified in eachcoronary artery ≥1.5 mm in mean vessel diameter. Non-calcified plaquedata reported is further broken down into low-density plaque, based onHU density thresholds. Low-Density Non-Calcified Plaque Volume (mm3)Low-Density--Non-Calcified Plaque is defined as plaque in between thelumen and vessel wall with an attenuation of less than or equal to 30 HUor as defined by the user and is reported in absolute measures by plaquevolume. Total Plaque Volume (mm3) Plaque volume is defined as plaque inbetween the lumen and vessel wall reported in absolute measures. Thetotal plaque volume is the sum total of all plaque identified in eachcoronary artery ≥ 1.5 mm in mean vessel diameter or wherever the userplaces the “End” marker.

STENOSIS Term Definition Remodeling Index Remodeling Index is defined asthe mean vessel diameter at a denoted slice divided by the mean vesseldiameter at a reference slice. Greatest Diameter Stenosis (%) Thedeviation of the mean lumen diameter at the denoted slice from areference slice, expressed in percentage. Greatest Area Stenosis (%) Thedeviation of the lumen area at the denoted slice to a reference area,expressed in percentage

A quantitative variable that is used in the system and displayed onvarious portions of the user interface 600, for example, in reference tolow-density non-calcified plaque, non-calcified plaque, and calcifiedplaque, is the Hounsfield unit (HU). As is known, a Hounsfield Unitscale is a quantitative scale for describing radiation, and isfrequently used in reference to CT scans as a way to characterizeradiation attenuation and thus making it easier to define what a givenfinding may represent. A Hounsfield Unit measurement is presented inreference to a quantitative scale. Examples of Hounsfield Unitmeasurements of certain materials are shown in the following table:

Material HU Air -1000 Fat -50 Distilled Water 0 Soft Tissue +40 Blood+40 to 80 Calcified Plaques 350-1000+ Bone +1000

In an embodiment, information that the system determines relating tostenosis, atherosclerosis, and CAD-RADS details are included on panel800 of the user interface 600, as illustrated in FIG. 8A. By default,the CAD-RADS score may be unselected and requires the user to manuallyselect the score on the CAD-RADS page. Hovering over the “#” iconscauses the user interface 600 to provide more information about theselected output. To view more details about the stenosis,atherosclerosis, and CAD-RADS outputs, click the “View Details” buttonin the upper right of panel 800 - this will navigate to the applicabledetails page. In an embodiment, in the center of a centerpiece page viewof the user interface 600 there is a non-patient specific rendition of acoronary artery tree 805 (a “cartoon artery tree” 805) broken intosegments 805 a-805 r based on the SCCT coronary segmentation, asillustrated in panel 802 in FIG. 8C. All analyzed vessels are displayedin color according to the legend 806 based on the highest diameterstenosis within that vessel. Greyed out segments/vessels in the cartoonartery tree 805, for example, segment 805 q and 805 r, were notanatomically available or not analyzed in the system (all segments maynot exist in all patients). Per-territory and per-segment informationcan be viewed by clicking the territory above the tree (RCA, LM+LAD,etc.) using, for example, the user interface 600 selection buttons inpanel 801, as illustrated in FIGS. 8B and 8C. Or my selecting a segment805 a-805 r within the cartoon coronary tree 805.

Stenosis and atherosclerosis data displayed on the user interface inpanel 807 will update accordingly as various segments are selected, asillustrated in FIG. 8D. FIG. 8E illustrates an example of a portion ofthe per-territory summary panel 807 of the user interface. FIG. 8F alsoillustrates an example of portion of panel 807 showing the SMPR of aselected vessel and its associated statistics along the vessel atindicated locations (e.g., at locations indicated by a pointing deviceas it is moved along the SMPR visualization). That is, the userinterface 600 is configured to provide plaque details and stenosisdetails in an SMPR visualization in panel 809 and a pop-up panel 810that displays information as the user interface receives locationinformation long the displayed vessel from the user, e.g., via apointing device. The presence of a chronic total occlusion (CT) and/or astent are indicated at the vessel segment level. For example, FIG. 8Gillustrates the presence of a stent in the D1 segment. FIG. 8H indicatesthe presence of a CTO in the mRCA segment. Coronary dominance and anyanomalies can be displayed below the coronary artery tree as illustratedin FIG. 8I. The anomalies that were selected in the analysis can bedisplayed, for example, by “hovering” with a pointing device over the“details” button. If plaque thresholds were changed in the analysis, analert can be displayed on the user interface, or on a generated report,that indicates the plaque thresholds were changed. When anomalies arepresent, the coronary vessel segment 805 associated with each anomalywill appear detached from the aorta as illustrated in FIG. 8J. In anembodiment, a textual summary of the analysis can also be displayedbelow the coronary tree, for example, as illustrated in the panel 811 inFIG. 8K.

FIG. 9A illustrates an atherosclerosis panel 900 that can be displayedon the user interface, which displays a summary of atherosclerosisinformation based on the analysis. FIG. 9B illustrates the vesselselection panel which can be used to select a vessel such that thesummary of atherosclerosis information is displayed on a per segmentbasis. The top section of the atherosclerosis panel 900 containsper-patient data, as illustrated in FIG. 9A. When a user “hovers” overthe “Segments with Calcified Plaque” on panel 901, or hovers over the“Segments with Non-Calcified Plaque” in panel 902, the segment nameswith the applicable plaque are displayed. Below the patient specificdata, users may access per-vessel and per-segment atherosclerosis databy clicking on one of the vessel buttons, illustrated in FIG. 9B.

FIG. 9C illustrates a panel 903, that can be generated and displayed onthe user interface, which shows atherosclerosis information determinedby the system on a per segment basis. The presence of positiveremodeling, the highest remodeling index, and the presence ofLow-Density-Non-Calcified Plaque are reported for each segment in thepanel 903 illustrated in FIG. 9C. For example, plaque data can bedisplayed below on a per-segment basis, and plaque composition volumescan be displayed on a per-segment in the panel 903 illustrated in FIG.9C.

FIG. 9D illustrates a panel 904 that can be displayed on the userinterface that contains stenosis per patient data. The top section ofthe stenosis panel 904 contains per-patient data. Further details abouteach count can be displayed by hovering with a pointing device over thenumbers, as illustrated in FIG. 9E. Vessels included in each territoryare shown in the table below:

Vessel Territory Segment Name LM (Left Main Artery) LM LAD (LeftAnterior Descending) pLAD mLAD dLAD D1 D2 RI LCx (Left CircumflexArtery) pCx LCx OM1 OM2 L-PLB L-PDA RCA (Right Coronary Artery) pRCAmRCA dRCA R-PLB R-PDA

In an embodiment, a percentage Diameter Stenosis bar graph 906 can begenerated and displayed in a panel 905 of the user interface, asillustrated in FIG. 9F. The percentage Diameter Stenosis bar graph 906displays the greatest diameter stenosis in each segment. If a CTO hasbeen marked on the segment, it will display as a 100% diameter stenosis.If more than one stenosis has been marked on a segment, the highestvalue outputs are displayed by default and the user can click into eachstenosis bar to view stenosis details and interrogate smaller stenosis(if present) within that segment. The user can also scroll through eachcross-section by dragging the grey button in the center of a SMPR viewof the vessel, and view the lumen diameter and % diameter stenosis ateach cross-section at any selected location, as illustrated in FIG. 9G.

FIG. 9H illustrates a panel showing categories of the one or morestenosis marked on the SMPR based on the analysis. Color can be used toenhance the displayed information. In an example, stenosis in the LM >=50% diameter stenosis are marked in red. As illustrated in a panel 907of the user interface in FIG. 9I, for each segment’s greatest percentagediameter stenosis the minimum luminal diameter and lumen diameter at thereference can be displayed when a pointing device is “hovered” above thegraphical vessel cross-section representation, as illustrated in FIG.9J. If a segment was not analyzed or is not anatomically present, thesegment will be greyed out and will display “Not Analyzed”. If a segmentwas analyzed but did not have any stenosis marked, the value willdisplay “N/A”.

FIG. 9K illustrates a panel 908 of the user interface that indicatesCADS-RADS score selection. The CAD-RADS panel displays the definitionsof CAD-RADS as defined by “Coronary Artery Disease - Reporting and DataSystem (CAD-RADS) An Expert Consensus Document of SCCT, ACR and NASCI:Endorsed by the ACC”. The user is in full control of selecting theCAD-RADS score. In an embodiment, no score will be suggested by thesystem. In another embodiment, a CAD-RADS score can be suggested. Once aCAD-RADS score is selected on this page, the score will display in bothcertain user interface panels and full text report pages. Once aCAD-RADS score is selected, the user has the option of selectingmodifiers and the presentation of symptoms. Once a presentation isselected, the interpretation, further cardiac investigation andmanagement guidelines can be displayed to the user on the userinterface, for example, as illustrated in the panel 909 illustrated inFIG. 9L. These guidelines reproduce the guidelines found in “CoronaryArtery Disease - Reporting and Data System (CAD-RADS) An ExpertConsensus Document of SCCT, ACR and NASCI: Endorsed by the ACC.”

FIGS. 9M and 9N illustrate tables that can be generated and displayed ona panel of the user interface, and/or included in a report. FIG. 9Millustrates quantitative stenosis and vessel outputs. FIG. 9Nillustrates quantitative plaque outputs. In these quantitative tables, auser can view quantitative per-segment stenosis and atherosclerosisoutputs from the system analysis. The quantitative stenosis and vesseloutputs table (FIG. 9M) includes information for the evaluated arteriesand segments. Totals are given for each vessel territory. Informationcan include, for example, length, vessel volume, lumen volume, totalplaque volume, maximum diameter stenosis, maximum area stenosis, andhighest remodeling index. The quantitative plaque outputs table (FIG.9N) includes information for the evaluated arteries and segments.Information can include, for example, total plaque volume, totalcalcified plaque volume, non-calcified plaque volume, low-densitynon-calcified plaque volume, and total non-calcified plaque volume. Theuser is also able to download a PDF or CSV file of the quantitativeoutputs is a full text Report. The full text Report presents a textualsummary of the atherosclerosis, stenosis, and CAD-RADS measures. Theuser can edit the report, as desired. Once the user chooses to edit thereport, the report will not update the CAD-RADS selection automatically.

FIG. 10 is a flowchart illustrating a process 1000 for analyzing anddisplaying CT images and corresponding information. At block 1005, theprocess 1000 stores computer-executable instructions, a set of CT imagesof a patient’s coronary vessels, vessel labels, and artery informationassociated with the set of CT images including information of stenosis,plaque, and locations of segments of the coronary vessels. All of thesteps of the process can be performed by embodiments of the systemdescribed herein, for example, on embodiments of the systems describedin FIG. 13 . For example, by one or more computer hardware processors incommunication with the one or more non-transitory computer storagemediums, executing the computer-executable instructions stored on one ormore non-transitory computer storage mediums. In various embodiments,the user interface can include one or more portions, or panels, that areconfigured to display one or more of images, in various views (e.g.,SMPR, CMPR, cross-sectional, axial, sagittal, coronal, etc.) related tothe CT images of a patient’s coronary arteries, a graphicalrepresentation of coronary arteries, features (e.g., a vessel wall, thelumen, the centerline, the stenosis, plaque, etc.) that have beenextracted or revised by machine learning algorithm or by an analyst, andinformation relating to the CT images that has been determined by thesystem, by an analyst, or by an analyst interacting with the system(e.g., measurements of features in the CT images. In variousembodiments, panels of the user interface can be arranged differentlythan what is described herein and what is illustrated in thecorresponding figures. A user can make an input to the user interfaceusing a pointing device or a user’s finger on a touchscreen. In anembodiment, the user interface can receive input by determining theselection of a button/icon/portion of the user interface. In anembodiment, the user interface can receive an input in a defined fieldof the user interface.

At block 1010, the process 1000 can generate and display in a userinterface a first panel including an artery tree comprising athree-dimensional (3D) representation of coronary vessels based on theCT images and depicting coronary vessels identified in the CT images,and depicting segment labels, the artery tree not including heart tissuebetween branches of the artery tree. An example of such an artery tree602 is shown in panel 601 in FIG. 6A. In various embodiments, panel 601can be positioned in locations of the user interface 600 other than whatis shown in FIG. 6A.

At block 1015, the process 1000 can receive a first input indicating aselection of a coronary vessel in the artery tree in the first panel.For example, the first input can be received by the user interface 600of a vessel in the artery tree 602 in panel 601. At block 1020, inresponse to the first input, the process 1000 can generate and displayon the user interface a second panel illustrating at least a portion ofthe selected coronary vessel in at least one straightened multiplanarvessel (SMPR) view. In an example, the SMPR view is displayed in panel604 of FIG. 6A.

At block 1025, the process 1000 can generate and display on the userinterface a third panel showing a cross-sectional view of the selectedcoronary vessel, the cross-sectional view generated using one of the setof CT images of the selected coronary vessel. Locations along the atleast one SMPR view are each associated with one of the CT images in theset of CT images such that a selection of a particular location alongthe coronary vessel in the at least one SMPR view displays theassociated CT image in the cross-sectional view in the third panel. Inan example, the cross-sectional view can be displayed in panel 606 asillustrated in FIG. 6A. At block 1035, the process 1000 can receive asecond input on the user interface indicating a first location along theselected coronary artery in the at least one SMPR view. In an example,user may use a pointing device to select a different portion of thevessel shown in the SMPR view in panel 604. At block 1030, the process1000, in response to the second input, displays the associated CT scanassociated in the cross-sectional view in the third panel, panel 606.That is, the cross-sectional view that correspond to the first input isreplaced by the cross-sectional view that corresponds to the secondinput on the SMPR view.

Normalization Device

In some instances, medical images processed and/or analyzed as describedthroughout this application can be normalized using a normalizationdevice. As will be described in more detail in this section, thenormalization device may comprise a device including a plurality ofsamples of known substances that can be placed in the medical imagefield of view so as to provide images of the known substances, which canserve as the basis for normalizing the medical images. In someinstances, the normalization device allows for direct within imagecomparisons between patient tissue and/or other substances (e.g.,plaque) within the image and known substances within the normalizationdevice.

As mentioned briefly above, in some instances, medical imaging scannersmay produce images with different scalable radiodensities for the sameobject. This, for example, can depend not only on the type of medicalimaging scanner or equipment used but also on the scan parameters and/orenvironment of the particular day and/or time when the scan was taken.As a result, even if two different scans were taken of the same subject,the brightness and/or darkness of the resulting medical image may bedifferent, which can result in less than accurate analysis resultsprocessed from that image. To account for such differences, in someembodiments, the normalization device comprising one or more knownsamples of known materials can be scanned together with the subject, andthe resulting image of the one or more known elements can be used as abasis for translating, converting, and/or normalizing the resultingimage.

Normalizing the medical images that will be analyzed can be beneficialfor several reasons. For example, medical images can be captured under awide variety of conditions, all of which can affect the resultingmedical images. In instances where the medical imager comprises a CTscanner, a number of different variables can affect the resulting image.Variable image acquisition parameters, for example, can affect theresulting image. Variable image acquisition parameters can comprise oneor more of a kilovoltage (kV), kilovoltage peak (kVp), a milliamperage(mA), or a method of gating, among others. In some embodiments, methodsof gating can include prospective axial triggering, retrospective ECGhelical gating, and fast pitch helical, among others. Varying any ofthese parameters, may produce slight differences in the resultingmedical images, even if the same subject is scanned.

Additionally, the type of reconstruction used to prepare the image afterthe scan may provide differences in medical images. Example types ofreconstruction can include iterative reconstruction, non-iterativereconstruction, machine learning-based reconstruction, and other typesof physics-based reconstruction among others. FIGS. 11A-11D illustratedifferent images reconstructed using different reconstructiontechniques. In particular, FIG. 11A illustrates a CT image reconstructedusing filtered back projection, while FIG. 11B illustrates the same CTimage reconstructed using iterative reconstruction. As shown, the twoimages appear slightly different. The normalization device describedbelow can be used to help account for these differences by providing amethod for normalizing between the two. FIG. 11C illustrates a CT imagereconstructed by using iterative reconstruction, while FIG. 11Dillustrates the same image reconstructed using machine learning. Again,one can see that the images include slight differences, and thenormalization device described herein can advantageously be useful innormalizing the images to account for the two differences.

As another example, various types of image capture technologies can beused to capture the medical images. In instances where the medicalimager comprises a CT scanner, such image capture technologies mayinclude a dual source scanner, a single source scanner, dual energy,monochromatic energy, spectral CT, photon counting, and differentdetector materials, among others. As before, images captured usingdifference parameters may appear slightly different, even if the samesubject is scanned. In addition to CT scanners, other types of medicalimagers can also be used to capture medical images. These can include,for example, x-ray, ultrasound, echocardiography, intravascularultrasound (IVUS), MR imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS). Use of the normalization device can facilitatenormalization of images such that images captured on these differentimaging devices can be used in the methods and systems described herein.

Additionally, new types of medical imaging technologies are currentlybeing developed. Use of the normalization device can allow the methodsand systems described herein to be used even with medical imagingtechnologies that are currently being developed or that will bedeveloped in the future. Use of different or emerging medical imagingtechnologies can also cause slight differences between images.

Another factor that can cause differences in medical images that can beaccounted for using the normalization device can be use of differentcontrast agents during medical imaging. Various contrast agentscurrently exist, and still others are under development. Use of thenormalization device can facilitate normalization of medical imagesregardless of the type of contrast agent used and even in instanceswhere no contrast agent is used.

These slight differences can, in some instances, negatively impactanalysis of the image, especially where analysis of the image isperformed by artificial intelligence or machine learning algorithms thatwere trained or developed using medical images captured under differentconditions. In some embodiments, the methods and systems describedthroughout this application for analyzing medical images can include theuse of artificial intelligence and/or machine learning algorithms. Suchalgorithms can be trained using medical images. In some embodiments, themedical images that are used to train these algorithms can include thenormalization device such that the algorithms are trained based onnormalized images. Then, by normalizing subsequent images by alsoincluding the normalization device in those images, the machine learningalgorithms can be used to analyze medical images captured under a widevariety of parameters, such as those described above.

In some embodiments, the normalization device described herein isdistinguishable from a conventional phantom. In some instances,conventional phantoms can be used to verify if a CT machine is operatingin a correct manner. These conventional phantoms can be usedperiodically to verify the calibration of the CT machine. For example,in some instances, conventional phantoms can be used prior to each scan,weekly, monthly, yearly, or after maintenance on the CT machine toensure proper functioning and calibration. Notably, however, theconventional phantoms do not provide a normalization function thatallows for normalization of the resulting medical images acrossdifferent machines, different parameters, different patients, etc.

In some embodiments, the normalization device described herein canprovide this functionality. The normalization device can allow for thenormalization of CT data or other medical imaging data generated byvarious machine types and/or for normalization across differentpatients. For example, different CT devices manufactured by variousmanufacturers, can produce different coloration and/or different grayscale images. In another example, some CT scanning devices can producedifferent coloration and/or different gray scale images as the CTscanning device ages or as the CT scanning device is used or based onthe environmental conditions surrounding the device during the scanning.In another example, patient tissue types or the like can cause differentcoloration and/or gray scale levels to appear differently in medicalimage scan data. Normalization of CT scan data can be important in orderto ensure that processing of the CT scan data or other medical imagingdata is consistent across various data sets generated by variousmachines or the same machines used at different times and/or acrossdifferent patients. In some embodiments, the normalization device needsto be used each time a medical image scan is performed because scanningequipment can change over time and/or patients are different with eachscan. In some embodiments, the normalization device is used inperforming each and every scan of patient in order to normalize themedical image data of each patient for the AI algorithm(s) used toanalyze the medical image data of the patient. In other words, in someembodiments, the normalization device is used to normalize to eachpatient as opposed to each scanner. In some embodiments, thenormalization device may have different known materials with differentdensities adjacent to each other (e.g., as described with reference toFIG. 12F). This configuration may address an issue present in some CTimages where the density of a pixel influences the density of theadjacent pixels and that influence changes with the density of each ofthe individual pixel. One example of such an embodiment can includedifferent contrast densities in the coronary lumen influencing thedensity of the plaque pixels. The normalization device can address thisissue by having known volumes of known substances to help to correctlyevaluate volumes of materials/lesions within the image correcting insome way the influence of the blooming artifact on quantitative CT imageanalysis/measures. In some instances, the normalization device mighthave moving known materials with known volume and known and controllablemotion. This may allow to exclude or reduce the effect of motion onquantitative CT image analysis/measures.

Accordingly, the normalization device, in some embodiments, is not aphantom in the traditional sense because the normalization device is notjust calibrating to a particular scanner but is also normalizing for aspecific patient at a particular time in a particular environment for aparticular scan, for particular scan image acquisition parameters,and/or for specific contrast protocols. Accordingly, in someembodiments, the normalization device can be considered a reversephantom. This can be because, rather than providing a mechanism forvalidating a particular medical imager as a conventional phantom would,the normalization device can provide a mechanism for normalizing orvalidating a resulting medical image such that it can be compared withother medical images taken under different conditions. In someembodiments, the normalization device is configured to normalize themedical image data being examined with the medical image data used totrain, test, and/or validate the AI algorithms used for analyzing the tobe examined medical image data.

In some embodiments, the normalization of medical scanning data can benecessary for the AI processing methods disclosed herein because in someinstances AI processing methods can only properly process medicalscanning data when the medical scanning data is consistent across allmedical scanning data being processed. For example, in situations wherea first medical scanner produces medical images showing fatty materialas dark gray or black, whereas a second medical scanner produces medicalimage showing the same fatty material as medium or light gray, then theAI processing methodologies of the systems, methods, and devicesdisclosed herein may misidentify and/or not fully identify the fattymaterials in one set or both sets of the medical images produced by thefirst and second medical scanners. This can be even more problematic asthe relationship of specific material densities may not be constant, andeven may change in an non linear way depending on the material and onthe scanning parameters. In some embodiments, the normalization deviceenables the use of AI algorithms trained on certain medical scannerdevices to be used on medical images generated by next-generationmedical scanner devices that may have not yet even been developed.

FIG. 12A is a block diagram representative of an embodiment of anormalization device 1200 that can be configured to normalize medicalimages for use with the methods and systems described herein. In theillustrated embodiment, the normalization device 1200 can include asubstrate 1202. The substrate 1202 can provide the body or structure forthe normalization device 1200. In some embodiments, the normalizationdevice 1200 can comprise a square or rectangular or cube shape, althoughother shapes are possible. In some embodiments, the normalization device1200 is configured to be bendable and/or be self-supporting. Forexample, the substrate 1202 can be bendable and/or self-supporting. Abendable substrate 1202 can allow the normalization device to fit to thecontours of a patient’s body. In some embodiments, the substrate 1202can comprise one or more fiducials 1203. The fiducials 1203 can beconfigured to facilitate determination of the alignment of thenormalization device 1200 in an image of the normalization device suchthat the position in the image of each of the one or more compartmentsholding samples of known materials can be determined.

The substrate 1202 can also include a plurality of compartments (notshown in FIG. 12A, but see, for example, compartments 1216 of FIGS.12C-12F). The compartments 1216 can be configured to hold samples ofknown materials, such as contrast samples 1204, studied variable samples1206, and phantom samples 1208. In some embodiments, the contrastsamples 1204 comprise samples of contrast materials used during captureof the medical image. In some embodiments, the samples of the contrastmaterials 1204 comprise one or more of iodine, Gad, Tantalum, Tungsten,Gold, Bismuth, or Ytterbium. These samples can be provided within thecompartments 1216 of the normalization device 1200 at variousconcentrations. The studied variable samples 1206 can includes samplesof materials representative of materials to be analyzed systems andmethods described herein. In some examples, the studied variable samples1206 comprise one or more of calcium 1000HU, calcium 220HU, calcium150HU, calcium 130HU, and a low attenuation (e.g., 30 HU) material.Other studied variable samples 1206 provided at different concentrationscan also be included. In general, the studied variable samples 1206 cancorrespond to the materials for which the medical image is beinganalyzed. The phantom samples 1208 can comprise samples of one or morephantom materials. In some examples, the phantom samples 1208 compriseone or more of water, fat, calcium, uric acid, air, iron, or blood.Other phantom samples 1208 can also be used.

In some embodiments, the more materials contained in the normalizationdevice 1200, or the more compartments 1216 with different materials inthe normalization device 1200, the better the normalization of the dataproduced by the medical scanner. In some embodiments, the normalizationdevice 1200 or the substrate 1202 thereof is manufactured from flexibleand/or bendable plastic. In some embodiments, the normalization device1200 is adapted to be positioned within or under the coils of an MRscanning device. In some embodiments, the normalization device 1200 orthe substrate 1202 thereof is manufactured from rigid plastic.

In the illustrated embodiment of FIG. 12A, the normalization device 1200also includes an attachment mechanism 1210. The attachment mechanism1210 can be used to attach the normalization device 1200 to the patient.For example, in some embodiments, the normalization device 1200 isattached to the patient near the coronary region to be imaged prior toimage acquisition. In some embodiments, the normalization device 1200can be adhered to the skin of a patient using an adhesive or Velcro orsome other fastener or glue. In some embodiments, the normalizationdevice 1200 can be applied to a patient like a bandage. For example, insome embodiments, a removable Band-Aid or sticker is applied to the skinof the patient, wherein the Band-Aid can comprise a Velcro outwardfacing portion that allows the normalization device having acorresponding Velcro mating portion to adhere to the Band-Aid or stickerthat is affixed to the skin of the patient (see, for example, thenormalization device of FIG. 12G, described below).

In some embodiments, the attachment mechanism 1210 can be omitted, suchthat the normalization device 1200 need not be affixed to the patient.Rather, in some embodiments, the normalization device can be placed in amedical scanner with or without a patient. In some embodiments, thenormalization device can be configured to be placed alongside a patientwithin a medical scanner.

In some embodiments, the normalization device 1200 can be a reusabledevice or be a disposable one-time use device. In some embodiments, thenormalization device 1200 comprises an expiration date, for example, thedevice can comprise a material that changes color to indicate expirationof the device, wherein the color changes over time and/or after acertain number of scans or an amount of radiation exposure (see, forexample, FIGS. 12H and 12I, described below). In some embodiments, thenormalization device 1200 requires refrigeration between uses, forexample, to preserve one or more of the samples contained therein. Insome embodiments, the normalization device 1200 can comprise anindicator, such as a color change indicator, that notifies the user thatthe device has expired due to heat exposure or failure to refrigerate.

In certain embodiments, the normalization device 1200 comprises amaterial that allows for heat transfer from the skin of the patient inorder for the materials within the normalization device 1200 to reachthe same or substantially the same temperature of the skin of thepatient because in some cases the temperature of the materials canaffect the resulting coloration or gray-scale of the materials producedby the image scanning device. For example, the substrate 1202 cancomprise a material with a relatively high heat transfer coefficient tofacilitate heat transfer from the patient to the samples within thesubstrate 1202. In some embodiments, the normalization device 1200 canbe removably coupled to a patient’s skin by using an adhesive that canallow the device to adhere to the skin of a patient.

In some embodiments, the normalization device 1200 can be used in theimaging field of view or not in the imaging field of view. In someembodiments, the normalization device 1200 can be imaged simultaneouslywith the patient image acquisition or sequentially. Sequential use cancomprise first imaging the normalization device 1200 and the imaging thepatient shortly thereafter using the same imaging parameters (or viceversa). In some embodiments, the normalization device 1200 can be staticor programmed to be in motion or movement in sync with the imageacquisition or the patient’s heart or respiratory motion. In someembodiments, the normalization device 1200 can utilize comparison toimage domain-based data or projection domain-based data. In someembodiments, the normalization device 1200 can be a 2D (area), or 3D(volume), or 4D (changes with time) device. In some embodiments, two ormore normalization devices 1200 can be affixed to and/or positionedalongside a patient during medical image scanning in order to accountfor changes in coloration and/or gray scale levels at different depthswithin the scanner and/or different locations within the scanner.

In some embodiments, the normalization device 1200 can comprise one ormore layers, wherein each layer comprises compartments for holding thesame or different materials as other layers of the device. FIG. 12B, forexample, illustrates a perspective view of an embodiment of anormalization device 1200 including a multilayer substrate 1202. In theillustrated embodiment, the substrate 1202 comprises a first layer 1212and a second layer 1214. The second layer 1214 can be positioned abovethe first layer 1212. In other embodiments, one or more additionallayers may be positioned above the second layer 1214. Each of the layers1212, 1214 can be configured with compartments for holding the variousknown samples, as shown in FIG. 12C. In some embodiments, the variouslayers 1212, 1214 of the normalization device 1200 allow fornormalization at various depth levels for various scanning machines thatperform three-dimensional scanning, such as MR and ultrasound. In someembodiments, the system can be configured to normalize by averaging ofcoloration and/or gray scale level changes in imaging characteristicsdue to changes in depth.

FIG. 12C is a cross-sectional view of the normalization device 1200 ofFIG. 12B illustrating various compartments 1216 positioned therein forholding samples of known materials for use during normalization. Thecompartments 1216 can be configured to hold, for example, the contrastsamples 1204, the studied variable samples 1206, and the phantom samples1208 illustrated in FIG. 12A. The compartments 1216 may comprise spaces,pouches, cubes, spheres, areas, or the like, and within each compartment1216 there is contained one or more compounds, fluids, substances,elements, materials, and the like. In some embodiments, each of thecompartments 1216 can comprise a different substance or material. Insome embodiments, each compartment 1216 is air-tight and sealed toprevent the sample, which may be a liquid, from leaking out.

Within each layer 1212, 1214, or within the substrate 1202, thenormalization device 1200 may include different arrangements for thecompartments 1216. FIG. 12D illustrates a top down view of an examplearrangement of a plurality of compartments 1216 within the normalizationdevice 1200. In the illustrated embodiment, the plurality ofcompartments 1216 are arranged in a rectangular or grid-like pattern.FIG. 12E illustrates a top down view of another example arrangement of aplurality of compartments 1216 within a normalization device 1200. Inthe illustrated embodiment, the plurality of compartments 1216 arearranged in a circular pattern. Other arrangements are also possible.

FIG. 12F is a cross-sectional view of another embodiment of anormalization device 1200 illustrating various features thereof,including adjacently arranged compartments 1216A, self-sealing fillablecompartments 1216B, and compartments of various sizes and shapes 1216C.As shown in FIG. 12F, one or more of the compartments 1216A can bearranged so as to be adjacent to each other so that materials within thecompartments 1216A can be in contact with and/or in close proximity tothe materials within the adjacent compartments 1216A. In someembodiments, the normalization device 1200 comprises high densitymaterials juxtaposed to low density materials in order to determine howa particular scanning device displays certain materials, therebyallowing normalization across multiple scanning devices. In someembodiments, certain materials are positioned adjacent or near othermaterials because during scanning certain materials can influence eachother. Examples of materials that can be placed in adjacently positionedcompartments 1216A can include iodine, air, fat material, tissue,radioactive contrast agent, gold, iron, other metals, distilled water,and/or water, among others.

In some embodiments, the normalization device 1200 is configured receivematerial and/or fluid such that the normalization device isself-sealing. Accordingly, FIG. 12F illustrates compartments 1216B thatare self-sealing. These can allow a material to be injected into thecompartment 1216B and then sealed therein. For example, a radioactivecontrast agent can be injected in a self-sealing manner into acompartment 1216B of the normalization device 1200, such that themedical image data generated from the scanning device can be normalizedover time as the radioactive contrast agent decays over time during thescanning procedure. In some embodiments, the normalization device can beconfigured to contain materials specific for a patient and/or a type oftissue being analyzed and/or a disease type and/or a scanner machinetype.

In some embodiments, the normalization device 1200 can be configuredmeasure scanner resolution and type of resolution by configuring thenormalization device 1200 with a plurality of shapes, such as a circle.Accordingly, the compartments 1216C can be provided with differentshapes and sizes. FIG. 12F illustrates an example wherein compartments1216C are provided with different shapes (cubic and spherical) anddifferent sizes. In some embodiments, all compartments 1216 can be thesame shape and size.

In some embodiments, the size of one or more compartment 1216 of thenormalization device 1200 can be configured or selected to correspond tothe resolution of the medical image scanner. For example, in someembodiments, if the spatial resolution of a medical image scanner is 0.5mm × 0.5 mm × 0.5 mm, then the dimension of the compartments of thenormalization device can also be 0.5 mm × 0.5 mm × 0.5 mm. In someembodiments, the sizes of the compartments range from 0.5 mm to 0.75 mm.In some embodiments, the width of the compartments of the normalizationdevice can be about 0.1 mm, about 0.15 mm, about 0.2 mm, about 0.25 mm,about 0.3 mm, about 0.35 mm, about 0.4 mm, about 0.45 mm, about 0.5 mm,about 0.55 mm, about 0.6 mm, about 0.65 mm, about 0.7 mm, about 0.75 mm,about 0.8 mm, about 0.85 mm, about 0.9 mm, about 0.95 mm, about 1.0 mm,and/or within a range defined by two of the aforementioned values. Insome embodiments, the length of the compartments of the normalizationdevice can be about 0.1 mm, about 0.15 mm, about 0.2 mm, about 0.25 mm,about 0.3 mm, about 0.35 mm, about 0.4 mm, about 0.45 mm, about 0.5 mm,about 0.55 mm, about 0.6 mm, about 0.65 mm, about 0.7 mm, about 0.75 mm,about 0.8 mm, about 0.85 mm, about 0.9 mm, about 0.95 mm, about 1.0 mm,and/or within a range defined by two of the aforementioned values. Insome embodiments, the height of the compartments of the normalizationdevice can be about 0.1 mm, about 0.15 mm, about 0.2 mm, about 0.25 mm,about 0.3 mm, about 0.35 mm, about 0.4 mm, about 0.45 mm, about 0.5 mm,about 0.55 mm, about 0.6 mm, about 0.65 mm, about 0.7 mm, about 0.75 mm,about 0.8 mm, about 0.85 mm, about 0.9 mm, about 0.95 mm, about 1.0 mm,and/or within a range defined by two of the aforementioned values.

In some embodiments, the dimensions of each of the compartments 1216 inthe normalization device 1200 are the same or substantially the same forall of the compartments 1216. In some embodiments, the dimensions ofsome or all of the compartments 1216 in the normalization device 1200can be different from each other in order for a single normalizationdevice 1200 to have a plurality of compartments having differentdimensions such that the normalization device 1200 can be used invarious medical image scanning devices having different resolutioncapabilities (for example, as illustrated in FIG. 12F). In someembodiments, a normalization device 1200 having a plurality ofcompartments 1216 with differing dimensions enable the normalizationdevice to be used to determine the actual resolution capability of thescanning device. In some embodiments, the size of each compartment 1216may extend up to 10 mm, and the sizes of each compartment may bevariable depending upon the material contained within.

In the illustrated embodiment of FIGS. 12C and 12F, the normalizationdevice 1200 includes an attachment mechanism 1210 which includes anadhesive surface 1218. The adhesive surface 1218 can be configured toaffix (e.g., removably affix) the normalization device 1200 to the skinof the patient. FIG. 12G is a perspective view illustrating anembodiment of an attachment mechanism 1210 for a normalization device1200 that uses hook and loop fasteners 1220 to secure a substrate of thenormalization device to a fastener of the normalization device 1200. Inthe illustrated embodiment, an adhesive surface 1218 can be configuredto be affixed to the patient. The adhesive surface 1218 can include afirst hook and loop fastener 1220. A corresponding hook and loopfastener 1220 can be provided on a lower surface of the substrate 1202and used to removably attach the substrate 1202 to the adhesive surface1218 via the hook and loop fasteners 1220.

FIGS. 12H and 12I illustrate an embodiment of a normalization device1200 that includes an indicator 1222 configured to indicate anexpiration status of the normalization device 1200. The indicator 1222can comprise a material that changes color or reveals a word to indicateexpiration of the device, wherein the color or text changes or appearsover time and/or after a certain number of scans or an amount ofradiation exposure. FIG. 12H illustrates the indicator 1222 in a firststate representative of a non-expired state, and FIG. 12I illustratesthe indicator 1222 in a second state representative of an expired state.In some embodiments, the normalization device 1200 requiresrefrigeration between uses. In some embodiments, the indicator 1222,such as a color change indicator, can notify the user that the devicehas expired due to heat exposure or failure to refrigerate.

In some embodiments, the normalization device 1200 can be used with asystem configured to set distilled water to a gray scale value of zero,such that if a particular medical image scanning device registers thecompartment of the normalization device 1200 comprising distilled wateras having a gray scale value of some value other than zero, then thesystem can utilize an algorithm to transpose or transform the registeredvalue to zero. In some embodiments, the system is configured to generatea normalization algorithm based on known values established forparticular substances in the compartments of the normalization device1200, and on the detected/generated values by a medical image scanningdevice for the same substances in the compartments 1216 of thenormalization device 1200. In some embodiments, the normalization device1200 can be configured to generate a normalization algorithm based on alinear regression model to normalize medical image data to be analyzed.In some embodiments, the normalization device 1200 can be configured togenerate a normalization algorithm based on a non-linear regressionmodel to normalize medical image data to be analyzed. In someembodiments, the normalization device 1200 can be configured to generatea normalization algorithm based on any type of model or models, such asan exponential, logarithmic, polynomial, power, moving average, and/orthe like, to normalize medical image data to be analyzed. In someembodiments, the normalization algorithm can comprise a two-dimensionaltransformation. In some embodiments, the normalization algorithm cancomprise a three-dimensional transformation to account for other factorssuch as depth, time, and/or the like.

By using the normalization device 1200 to scan known substances usingdifferent machines or the same machine at different times, the systemcan normalize CT scan data across various scanning machines and/or thesame scanning machine at different times. In some embodiments, thenormalization device 1200 disclosed herein can be used with any scanningmodality including but not limited to x-ray, ultrasound, echocardiogram,magnetic resonance (MR), optical coherence tomography (OCT),intravascular ultrasound (IVUS) and/or nuclear medicine imaging,including positron-emission tomography (PET) and single photon emissioncomputed tomography (SPECT).

In some embodiments, the normalization device 1200 contains one or morematerials that form plaque (e.g., studied variable samples 1206) and oneor more materials that are used in the contrast that is given to thepatient through a vein during examination (e.g., contrast samples 1204).In some embodiments, the materials within the compartments 1216 includeiodine of varying concentrations, calcium of varying densities,non-calcified plaque materials or equivalents of varying densities,water, fat, blood or equivalent density material, iron, uric acid, air,gadolinium, tantalum, tungsten, gold, bismuth, ytterbium, and/or othermaterial. In some embodiments, the training of the AI algorithm can bebased at least in part on data relating to the density in the images ofthe normalization device 1200. As such, in some embodiments, the systemcan have access to and/or have stored pre-existing data on how thenormalization device 1200 behaved or was shown in one or more imagesduring the training of the AI algorithm. In some embodiments, the systemcan use such prior data as a baseline to determine the difference withhow the normalization device 1200 behaves in the new or current CT scanto which the AI algorithm is applied to. In some embodiments, thedetermined difference can be used to calibrate, normalize, and/or mapone or more densities in recently acquired image(s) to one or moreimages that were obtained and/or used during training of the AIalgorithm.

As a non-limiting example, in some embodiments, the normalization device1200 comprises calcium. If, for example, the calcium in the CT ornormalization device 1200 that was used to train the AI algorithm(s)showed a density of 300 Hounsfield Units (HU), and if the same calciumshowed a density of 600 HU in one or more images of a new scan, then thesystem, in some embodiments, may be configured to automatically divideall calcium densities in half to normalize or transform the new CTimage(s) to be equivalent to the old CT image(s) used to train the AIalgorithm.

In some embodiments, as discussed above, the normalization device 1200comprises a plurality of all materials that may be relevant, which canbe advantageous as different materials can change densities in differentamounts across scans. For example, if the density of calcium changes 2Xacross scans, the density of fat may change around 10% across the samescans. As such, it can be advantageous for the normalization device 1200to comprise a plurality of materials, such as for example one or morematerials that make up plaque, blood, contrast, and/or the like.

As described above, in some embodiments, the system can be configured tonormalize, map, and/or calibrate density readings and/or CT imagesobtained from a particular scanner and/or subject proportionallyaccording to changes or differences in density readings and/or CT imagesobtained from one or more materials of a normalization device 1200 usinga baseline scanner compared to density readings and/or CT imagesobtained from one or more same materials of a normalization device 1200using the particular scanner and/or subject. As a non-limiting example,for embodiments in which the normalization device 1200 comprisescalcium, the system can be configured to apply the same change indensity of known calcium between the baseline scan and the new scan, forexample 2X, to all other calcium readings of the new scan to calibrateand/or normalize the readings.

In some embodiments, the system can be configured to normalize, map,and/or calibrate density readings and/or CT images obtained from aparticular scanner and/or subject by averaging changes or differencesbetween density readings and/or CT images obtained from one or morematerials of a normalization device 1200 using a baseline scannercompared to density readings and/or CT images obtained from one or morematerials or areas of a subject using the same baseline scanner. As anon-limiting example, for embodiments in which the normalization device1200 comprises calcium, the system can be configured to determine adifference, or a ratio thereof, in density readings between calcium inthe normalization device 1200 and other areas of calcium in the subjectduring the baseline scan. In some embodiments, the system can beconfigured to similarly determine a difference, or a ratio thereof, indensity readings between calcium in the normalization device 1200 andother areas of calcium in the subject during the new scan; dividing thevalue of calcium from the device to the value of calcium anywhere elsein the image can cancel out any change as the difference in conditionscan affect the same material in the same manner.

In some embodiments, the device will account for scan parameters (suchas mA or kVp), type and number of x-ray sources within a scanner (suchas single source or dual source), temporal resolution of a scanner,spatial resolution of scanner or image, image reconstruction method(such as adaptive statistical iterative reconstruction, model-basediterative reconstruction, machine learning-based iterativereconstruction or similar); image reconstruction method (such as fromdifferent types of kernels, overlapping slices from retrospectiveECG-helical studies, non-overlapping slices from prospective axialtriggered studies, fast pitch helical studies, or half vs. full scanintegral reconstruction); contrast density accounting for internalfactors (such as oxygen, blood, temperature, and others); contrastdensity accounting for external factors (such as contrast density,concentration, osmolality and temporal change during the scan);detection technology (such as material, collimation and filtering);spectral imaging (such as polychromatic, monochromatic and spectralimaging along with material basis decomposition and single energyimaging); photon counting; and/or scanner brand and model.

In some embodiments, the normalization device 1200 can be applied to MRIstudies, and account for one or more of: type of coil; place ofpositioning, number of antennas; depth from coil elements; imageacquisition type; pulse sequence type and characteristics; fieldstrength, gradient strength, slew rate and other hardwarecharacteristics; magnet vendor, brand and type; imaging characteristics(thickness, matrix size, field of view, acceleration factor,reconstruction methods and characteristics, 2D, 3D, 4D [cine imaging,any change over time], temporal resolution, number of acquisitions,diffusion coefficients, method of populating k-space); contrast(intrinsic [oxygen, blood, temperature, etc.] and extrinsic types,volume, temporal change after administration); static or movingmaterials; quantitative imaging (including T1 T2 mapping, ADC,diffusion, phase contrast, and others); and/or administration ofpharmaceuticals during image acquisition.

In some embodiments, the normalization device 1200 can be applied toultrasound studies, and account for one or more of: type and machinebrands; transducer type and frequency; greyscale, color, and pulsed wavedoppler; B- or M-mode doppler type; contrast agent; field of view; depthfrom transducer; pulsed wave deformity (including elastography), angle;imaging characteristics (thickness, matrix size, field of view,acceleration factor, reconstruction methods and characteristics, 2D, 3D,4D [cine imaging, any change over time]; temporal resolution; number ofacquisitions; gain, and/or focus number and places, amongst others.

In some embodiments, the normalization device 1200 can be applied tonuclear medicine studies, such as PET or SPECT and account for one ormore of: type and machine brands; for PET/CT all CT applies; for PET/MRall MR applies; contrast (radiopharmaceutical agent types, volume,temporal change after administration); imaging characteristics(thickness, matrix size, field of view, acceleration factor,reconstruction methods and characteristics, 2D, 3D, 4D [cine imaging,any change over time]; temporal resolution; number of acquisitions;gain, and/or focus number and places, amongst others.

In some embodiments, the normalization device may have different knownmaterials with different densities adjacent to each other. This mayaddress any issue present in some CT images where the density of a pixelinfluences the density of the adjacent pixels and that influence changeswith the density of each of the individual pixel. One example of thisembodiment being different contrast densities in the coronary lumeninfluencing the density of the plaque pixels. In some embodiments, thenormalization device may include known volumes of known substances tohelp to correctly evaluate volumes of materials/lesions within the imagein order to correct the influence of the blooming artifact onquantitative CT image analysis/measures. In some embodiments, thenormalization device might have moving known materials with known volumeand known and controllable motion. This would allow to exclude or reducethe effect of motion on quantitative CT image analysis/measures.

In some embodiments, having a known material on the image in thenormalization device might also be helpful for material specificreconstructions from the same image. For example, it can be possible touse only one set of images to display only known materials, not needingmultiple kV/spectral image hardware.

FIG. 12J is a flowchart illustrating an example method 1250 fornormalizing medical images for an algorithm-based medical imaginganalysis such as the analyses described herein. Use of the normalizationdevice can improve accuracy of the algorithm-based medical imaginganalysis. The method 1250 can be a computer-implemented method,implemented on a system that comprises a processor and an electronicstorage medium. The method 1250 illustrates that the normalizationdevice can be used to normalize medical images captured under differentconditions. For example, at block 1252, a first medical image of acoronary region of a subject and the normalization device is accessed.The first medical image can be obtained non-invasively. Thenormalization device can comprise a substrate comprising a plurality ofcompartments, each of the plurality of compartments holding a sample ofa known material, for example as described above. At block 1254, asecond medical image of a coronary region of a subject and thenormalization device is captured. The second medical image can beobtained non-invasively. Although the method 1250 is described withreference to a coronary region of a patient, the method is alsoapplicable to all body parts and not only the vessels as the sameprinciples apply to all body parts, all time points and all imagingdevices. This can even include “live” type of images such as fluoroscopyor MR real time image.

As illustrated by the portion within the dotted lines, the first medicalimage and the second medical image can comprise at least one of thefollowing: (1) one or more first variable acquisition parametersassociated with capture of the first medical image differ from acorresponding one or more second variable acquisition parametersassociated with capture of the second medical image, (2) a first imagecapture technology used to capture the first medical image differs froma second image capture technology used to capture the second medicalimage, and (3) a first contrast agent used during the capture of thefirst medical image differs from a second contrast agent used during thecapture of the second medical image.

In some embodiments, the first medical image and the second medicalimage each comprise a CT image and the one or more first variableacquisition parameters and the one or more second variable acquisitionparameters comprise one or more of a kilovoltage (kV), kilovoltage peak(kVp), a milliamperage (mA), or a method of gating. In some embodiments,the method of gating comprises one of prospective axial triggering,retrospective ECG helical gating, and fast pitch helical. In someembodiments, the first image capture technology and the second imagecapture technology each comprise one of a dual source scanner, a singlesource scanner, dual energy, monochromatic energy, spectral CT, photoncounting, and different detector materials. In some embodiments, thefirst contrast agent and the second contrast agent each comprise one ofan iodine contrast of varying concentration or a non-iodine contrastagent. In some embodiments, the first image capture technology and thesecond image capture technology each comprise one of CT, x-ray,ultrasound, echocardiography, intravascular ultrasound (IVUS), MRimaging, optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), or near-field infrared spectroscopy (NIRS).

In some embodiments, a first medical imager that captures the firstmedical imager is different than a second medical image that capture thesecond medical image. In some embodiments, the subject of the firstmedical image is different than the subject of the first medical image.In some embodiments, wherein the subject of the first medical image isthe same as the subject of the second medical image. In someembodiments, wherein the subject of the first medical image is differentthan the subject of the second medical image. In some embodiments,wherein the capture of the first medical image is separated from thecapture of the second medical image by at least one day. In someembodiments, wherein the capture of the first medical image is separatedfrom the capture of the second medical image by at least one day. Insome embodiments, wherein a location of the capture of the first medicalimage is geographically separated from a location of the capture of thesecond medical image.

Accordingly, it is apparent that the first and second medical images canbe acquired under different conditions that can cause differencesbetween the two images, even if the subject of each image is the same.The normalization device can help to normalize and account for thesedifferences.

The method 1250 then moves to blocks 1262 and 1264, at which imageparameters of the normalization device within the first medical imageand which image parameters of the normalization device within the secondmedical image are identified, respectively. Due to differentcircumstances under which the first and second medical images werecaptured, the normalization device may appear differently in each image,even though the normalization device includes the same known samples.

Next, at blocks 1266 and 1268, the method generates a normalized firstmedical image for the algorithm-based medical imaging analysis based inpart on the first identified image parameters of the normalizationdevice within the first medical image and generates a normalized secondmedical image for the algorithm-based medical imaging analysis based inpart on the second identified image parameters of the normalizationdevice within the second medical image, respectively. In these blocks,each image is normalized based on the appearance or determinedparameters of the normalization device in each image.

In some embodiments, the algorithm-based medical imaging analysiscomprises an artificial intelligence or machine learning imaginganalysis algorithm, and the artificial intelligence or machine learningimaging analysis algorithm was trained using images that included thenormalization device.

System Overview

In some embodiments, the systems, devices, and methods described hereinare implemented using a network of one or more computer systems, such asthe one illustrated in FIG. 13 . FIG. 13 is a block diagram depicting anembodiment(s) of a system for medical image analysis, visualization,risk assessment, disease tracking, treatment generation, and/or patientreport generation.

As illustrated in FIG. 13 , in some embodiments, a main server system1302 is configured to perform one or more processes, analytics, and/ortechniques described herein, some of which relating to medical imageanalysis, visualization, risk assessment, disease tracking, treatmentgeneration, and/or patient report generation. In some embodiments, themain server system 1302 is connected via an electronic communicationsnetwork 1308 to one or more medical facility client systems 1304 and/orone or more user access point systems 1306. For example, in someembodiments, one or more medical facility client systems 1304 can beconfigured to access a medical image taken at the medical facility of asubject, which can then be transmitted to the main server system 1302via the network 1308 for further analysis. After analysis, in someembodiments, the analysis results, such as for example quantified plaqueparameters, assessed risk of a cardiovascular event, generated report,annotated and/or derived medical images, and/or the like, can betransmitted back to the medical facility client system 1304 via thenetwork 1308. In some embodiments, the analysis results, such as forexample quantified plaque parameters, assessed risk of a cardiovascularevent, generated report, annotated and/or derived medical images, and/orthe like, can be transmitted also to a user access point system 1306,such as a smartphone or other computing device of the patient orsubject. As such, in some embodiments, a patient can be allowed to viewand/or access a patient-specific report and/or other analyses generatedand/or derived by the system from the medical image on the patient’scomputing device.

In some embodiments, the main server system 1302 can comprise and/or beconfigured to access one or more modules and/or databases for performingthe one or more processes, analytics, and/or techniques describedherein. For example, in some embodiments, the main server system 1302can comprise an image analysis module 1310, a plaque quantificationmodule 1312, a fat quantification module 1314, an atherosclerosis,stenosis, and/or ischemia analysis module 1316, a visualization/GUImodule 1318, a risk assessment module 1320, a disease tracking module1322, a normalization module 1324, a medical image database 1326, aparameter database 1328, a treatment database 1330, a patient reportdatabase 1332, a normalization device database 1334, and/or the like.

In some embodiments, the image analysis module 1310 can be configured toperform one or more processes described herein relating to imageanalysis, such as for example vessel and/or plaque identification from araw medical image. In some embodiments, the plaque quantification module1312 can be configured to perform one or more processes described hereinrelating to deriving or generating quantified plaque parameters, such asfor example radiodensity, volume, heterogeneity, and/or the like ofplaque from a raw medical image. In some embodiments, the fatquantification module 1314 can be configured to perform one or moreprocesses described herein relating to deriving or generating quantifiedfat parameters, such as for example radiodensity, volume, heterogeneity,and/or the like of fat from a raw medical image. In some embodiments,the atherosclerosis, stenosis, and/or ischemia analysis module 1316 canbe configured to perform one or more processes described herein relatingto analyzing and/or generating an assessment or quantification ofatherosclerosis, stenosis, and/or ischemia from a raw medical image. Insome embodiments, the visualization / GUI module 1318 can be configuredto perform one or more processes described herein relating to derivingor generating one or more visualizations and/or GUIs, such as forexample a straightened view of a vessel identifying areas of good and/orbad plaque from a raw medical image. In some embodiments, the riskassessment module 1320 can be configured to perform one or moreprocesses described herein relating to deriving or generating riskassessment, such as for example of a cardiovascular event or diseasefrom a raw medical image. In some embodiments, the disease trackingmodule 1322 can be configured to perform one or more processes describedherein relating to tracking a plaque-based disease, such as for exampleatherosclerosis, stenosis, ischemia, and/or the like from a raw medicalimage. In some embodiments, the normalization module 1324 can beconfigured to perform one or more processes described herein relating tonormalizing and/or translating a medical image, for example based on amedical image of a normalization device comprising known materials, forfurther processing and/or analysis.

In some embodiments, the medical image database 1326 can comprise one ormore medical images that are used for one or more of the variousanalysis techniques and processes described herein. In some embodiments,the parameter database 1328 can comprise one or more parameters derivedfrom raw medical images by the system, such as for example one or morevessel morphology parameters, quantified plaque parameters, quantifiedfat parameters, and/or the like. In some embodiments, the treatmentdatabase 1328 can comprise one or more recommended treatments derivedfrom raw medical images by the system. In some embodiments, the patientreport database 1332 can comprise one or more patient-specific reportsderived from raw medical images by the system and/or one or morecomponents thereof that can be used to generate a patient-specificreport based on medical image analysis results. In some embodiments, thenormalization database 1334 can comprise one or more historical datapoints and/or datasets of normalizing various medical images and/or thespecific types of medical imaging scanners and/or specific scanparameters used to obtain those images, as well as previously usednormalization variables and/or translations for different medicalimages.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 14 . The example computer system 1402 is incommunication with one or more computing systems 1420 and/or one or moredata sources 1422 via one or more networks 1418. While FIG. 14illustrates an embodiment of a computing system 1402, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 1402 may be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 1402 can comprise a Medical Analysis, RiskAssessment, and Tracking Module 1414 that carries out the functions,methods, acts, and/or processes described herein. The Medical Analysis,Risk Assessment, and Tracking Module 1414 is executed on the computersystem 1402 by a central processing unit 1406 discussed further below.

In general the word “module,” as used herein, refers to logic embodiedin hardware or firmware or to a collection of software instructions,having entry and exit points. Modules are written in a program language,such as JAVA, C or C++, PYTHON or the like. Software modules may becompiled or linked into an executable program, installed in a dynamiclink library, or may be written in an interpreted language such asBASIC, PERL, LUA, or Python. Software modules may be called from othermodules or from themselves, and/or may be invoked in response todetected events or interruptions. Modules implemented in hardwareinclude connected logic units such as gates and flip-flops, and/or mayinclude programmable units, such as programmable gate arrays orprocessors.

Generally, the modules described herein refer to logical modules thatmay be combined with other modules or divided into sub-modules despitetheir physical organization or storage. The modules are executed by oneor more computing systems, and may be stored on or within any suitablecomputer readable medium, or implemented in-whole or in-part withinspecial designed hardware or firmware. Not all calculations, analysis,and/or optimization require the use of computer systems, though any ofthe above-described methods, calculations, processes, or analyses may befacilitated through the use of computers. Further, in some embodiments,process blocks described herein may be altered, rearranged, combined,and/or omitted.

The computer system 1402 includes one or more processing units (CPU)1406, which may comprise a microprocessor. The computer system 1402further includes a physical memory 1410, such as random access memory(RAM) for temporary storage of information, a read only memory (ROM) forpermanent storage of information, and a mass storage device 1404, suchas a backing store, hard drive, rotating magnetic disks, solid statedisks (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory,diskette, or optical media storage device. Alternatively, the massstorage device may be implemented in an array of servers. Typically, thecomponents of the computer system 1402 are connected to the computerusing a standards based bus system. The bus system can be implementedusing various protocols, such as Peripheral Component Interconnect(PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) andExtended ISA (EISA) architectures.

The computer system 1402 includes one or more input/output (I/O) devicesand interfaces 1412, such as a keyboard, mouse, touch pad, and printer.The I/O devices and interfaces 1412 can include one or more displaydevices, such as a monitor, that allows the visual presentation of datato a user. More particularly, a display device provides for thepresentation of GUIs as application software data, and multi-mediapresentations, for example. The I/O devices and interfaces 1412 can alsoprovide a communications interface to various external devices. Thecomputer system 1402 may comprise one or more multi-media devices 1408,such as speakers, video cards, graphics accelerators, and microphones,for example.

The computer system 1402 may run on a variety of computing devices, suchas a server, a Windows server, a Structure Query Language server, a UnixServer, a personal computer, a laptop computer, and so forth. In otherembodiments, the computer system 1402 may run on a cluster computersystem, a mainframe computer system and/or other computing systemsuitable for controlling and/or communicating with large databases,performing high volume transaction processing, and generating reportsfrom large databases. The computing system 1402 is generally controlledand coordinated by an operating system software, such as z/OS, Windows,Linux, UNIX, BSD, SunOS, Solaris, MacOS, or other compatible operatingsystems, including proprietary operating systems. Operating systemscontrol and schedule computer processes for execution, perform memorymanagement, provide file system, networking, and I/O services, andprovide a user interface, such as a graphical user interface (GUI),among other things.

The computer system 1402 illustrated in FIG. 14 is coupled to a network1418, such as a LAN, WAN, or the Internet via a communication link 1416(wired, wireless, or a combination thereof). Network 1418 communicateswith various computing devices and/or other electronic devices. Network1418 is communicating with one or more computing systems 1420 and one ormore data sources 1422. The Medical Analysis, Risk Assessment, andTracking Module 1414 may access or may be accessed by computing systems1420 and/or data sources 1422 through a web-enabled user access point.Connections may be a direct physical connection, a virtual connection,and other connection type. The web-enabled user access point maycomprise a browser module that uses text, graphics, audio, video, andother media to present data and to allow interaction with data via thenetwork 1418.

Access to the Medical Analysis, Risk Assessment, and Tracking Module1414 of the computer system 1402 by computing systems 1420 and/or bydata sources 1422 may be through a web-enabled user access point such asthe computing systems 1420 or data source’s 1422 personal computer,cellular phone, smartphone, laptop, tablet computer, e-reader device,audio player, or other device capable of connecting to the network 1418.Such a device may have a browser module that is implemented as a modulethat uses text, graphics, audio, video, and other media to present dataand to allow interaction with data via the network 1418.

The output module may be implemented as a combination of an all-pointsaddressable display such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. The output module may be implemented to communicate with inputdevices 1412 and they also include software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements, such as menus, windows, dialogue boxes, tool bars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the output module may communicate with a set ofinput and output devices to receive signals from the user.

The input device(s) may comprise a keyboard, roller ball, pen andstylus, mouse, trackball, voice recognition system, or pre-designatedswitches or buttons. The output device(s) may comprise a speaker, adisplay screen, a printer, or a voice synthesizer. In addition a touchscreen may act as a hybrid input/output device. In another embodiment, auser may interact with the system more directly such as through a systemterminal connected to the score generator without communications overthe Internet, a WAN, or LAN, or similar network.

In some embodiments, the system 1402 may comprise a physical or logicalconnection established between a remote microprocessor and a mainframehost computer for the express purpose of uploading, downloading, orviewing interactive data and databases online in real time. The remotemicroprocessor may be operated by an entity operating the computersystem 1402, including the client server systems or the main serversystem, and/or may be operated by one or more of the data sources 1422and/or one or more of the computing systems 1420. In some embodiments,terminal emulation software may be used on the microprocessor forparticipating in the micro-mainframe link.

In some embodiments, computing systems 1420 who are internal to anentity operating the computer system 1402 may access the MedicalAnalysis, Risk Assessment, and Tracking Module 1414 internally as anapplication or process run by the CPU 1406.

The computing system 1402 may include one or more internal and/orexternal data sources (for example, data sources 1422). In someembodiments, one or more of the data repositories and the data sourcesdescribed above may be implemented using a relational database, such asDB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well asother types of databases such as a flat-file database, an entityrelationship database, and object-oriented database, and/or arecord-based database.

The computer system 1402 may also access one or more databases 1422. Thedatabases 1422 may be stored in a database or data repository. Thecomputer system 1402 may access the one or more databases 1422 through anetwork 1418 or may directly access the database or data repositorythrough I/O devices and interfaces 1412. The data repository storing theone or more databases 1422 may reside within the computer system 1402.

In some embodiments, one or more features of the systems, methods, anddevices described herein can utilize a URL and/or cookies, for examplefor storing and/or transmitting data or user information. A UniformResource Locator (URL) can include a web address and/or a reference to aweb resource that is stored on a database and/or a server. The URL canspecify the location of the resource on a computer and/or a computernetwork. The URL can include a mechanism to retrieve the networkresource. The source of the network resource can receive a URL, identifythe location of the web resource, and transmit the web resource back tothe requestor. A URL can be converted to an IP address, and a DomainName System (DNS) can look up the URL and its corresponding IP address.URLs can be references to web pages, file transfers, emails, databaseaccesses, and other applications. The URLs can include a sequence ofcharacters that identify a path, domain name, a file extension, a hostname, a query, a fragment, scheme, a protocol identifier, a port number,a username, a password, a flag, an object, a resource name and/or thelike. The systems disclosed herein can generate, receive, transmit,apply, parse, serialize, render, and/or perform an action on a URL.

A cookie, also referred to as an HTTP cookie, a web cookie, an internetcookie, and a browser cookie, can include data sent from a websiteand/or stored on a user’s computer. This data can be stored by a user’sweb browser while the user is browsing. The cookies can include usefulinformation for websites to remember prior browsing information, such asa shopping cart on an online store, clicking of buttons, logininformation, and/or records of web pages or network resources visited inthe past. Cookies can also include information that the user enters,such as names, addresses, passwords, credit card information, etc.Cookies can also perform computer functions. For example, authenticationcookies can be used by applications (for example, a web browser) toidentify whether the user is already logged in (for example, to a website). The cookie data can be encrypted to provide security for theconsumer. Tracking cookies can be used to compile historical browsinghistories of individuals. Systems disclosed herein can generate and usecookies to access data of an individual. Systems can also generate anduse JSON web tokens to store authenticity information, HTTPauthentication as authentication protocols, IP addresses to tracksession or identity information, URLs, and the like.

Example Embodiments

The following are non-limiting examples of certain embodiments ofsystems and methods of characterizing coronary plaque. Other embodimentsmay include one or more other features, or different features, that arediscussed herein.

Embodiment 1: A computer-implemented method of quantifying andclassifying coronary plaque within a coronary region of a subject basedon non-invasive medical image analysis, the method comprising:accessing, by a computer system, a medical image of a coronary region ofa subject, wherein the medical image of the coronary region of thesubject is obtained non-invasively; identifying, by the computer systemutilizing a coronary artery identification algorithm, one or morecoronary arteries within the medical image of the coronary region of thesubject, wherein the coronary artery identification algorithm isconfigured to utilize raw medical images as input; identifying, by thecomputer system utilizing a plaque identification algorithm, one or moreregions of plaque within the one or more coronary arteries identifiedfrom the medical image of the coronary region of the subject, whereinthe plaque identification algorithm is configured to utilize raw medicalimages as input; determining, by the computer system, one or morevascular morphology parameters and a set of quantified plaque parametersof the one or more identified regions of plaque from the medical imageof the coronary region of the subject, wherein the set of quantifiedplaque parameters comprises a ratio or function of volume to surfacearea, heterogeneity index, geometry, and radiodensity of the one or moreregions of plaque within the medical image; generating, by the computersystem, a weighted measure of the determined one or more vascularmorphology parameters and the set of quantified plaque parameters of theone or more regions of plaque; and classifying, by the computer system,the one or more regions of plaque within the medical image as stableplaque or unstable plaque based at least in part on the generatedweighted measure of the determined one or more vascular morphologyparameters and the determined set of quantified plaque parameters,wherein the computer system comprises a computer processor and anelectronic storage medium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereinone or more of the coronary artery identification algorithm or theplaque identification algorithm comprises an artificial intelligence ormachine learning algorithm.

Embodiment 3: The computer-implemented method of any one of Embodiment 1or 2, wherein the plaque identification algorithm is configured todetermine the one or more regions of plaque by determining a vessel walland lumen wall of the one or more coronary arteries and determining avolume between the vessel wall and lumen wall as the one or more regionsof plaque.

Embodiment 4: The computer-implemented method of any one of Embodiments1-3, wherein the one or more coronary arteries are identified by size.

Embodiment 5: The computer-implemented method of any one of Embodiments1-4, wherein a ratio of volume to surface area of the one or moreregions of plaque below a predetermined threshold is indicative ofstable plaque.

Embodiment 6: The computer-implemented method of any one of Embodiments1-5, wherein a radiodensity of the one or more regions of plaque above apredetermined threshold is indicative of stable plaque.

Embodiment 7: The computer-implemented method of any one of Embodiments1-6, wherein a heterogeneity of the one or more regions of plaque belowa predetermined threshold is indicative of stable plaque.

Embodiment 8: The computer-implemented method of any one of Embodiments1-7, wherein the set of quantified plaque parameters further comprisesdiffusivity of the one or more regions of plaque.

Embodiment 9: The computer-implemented method of any one of Embodiments1-8, wherein the set of quantified plaque parameters further comprises aratio of radiodensity to volume of the one or more regions of plaque.

Embodiment 10: The computer-implemented method of any one of Embodiments1-9, further comprising generating, by the computer system, a proposedtreatment for the subject based at least in part on the classified oneor more regions of plaque.

Embodiment 11: The computer-implemented method of any one of Embodiments1-10, further comprising generating, by the computer system, anassessment of the subject for one or more of atherosclerosis, stenosis,or ischemia based at least in part on the classified one or more regionsof plaque.

Embodiment 12: The computer-implemented method of any one of Embodiments1-11, wherein the medical image comprises a Computed Tomography (CT)image.

Embodiment 13: The computer-implemented method of Embodiment 12, whereinthe medical image comprises a non-contrast CT image.

Embodiment 14: The computer-implemented method of Embodiment 12, whereinthe medical image comprises a contrast-enhanced CT image.

Embodiment 15: The computer-implemented method of any one of Embodiments1-11, wherein the medical image comprises a Magnetic Resonance (MR)image.

Embodiment 16: The computer-implemented method of any one of Embodiments1-11, wherein the medical image is obtained using an imaging techniquecomprising one or more of CT, x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), MR imaging, optical coherencetomography (OCT), nuclear medicine imaging, positron-emission tomography(PET), single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).

Embodiment 17: The computer-implemented method of any one of Embodiments1-16, wherein the heterogeneity index of one or more regions of plaqueis determined by generating a three-dimensional histogram ofradiodensity values across a geometric shape of the one or more regionsof plaque.

Embodiment 18: The computer-implemented method of any one of Embodiments1-17, wherein the heterogeneity index of one or more regions of plaqueis determined by generating spatial mapping of radiodensity valuesacross the one or more regions of plaque.

Embodiment 19: The computer-implemented method of any one of Embodiments1-18, wherein the set of quantified plaque parameters comprises apercentage composition of plaque comprising different radiodensityvalues.

Embodiment 20: The computer-implemented method of any one of Embodiments1-19, wherein the set of quantified plaque parameters comprises apercentage composition of plaque comprising different radiodensityvalues as a function of volume of plaque.

Embodiment 21: The computer-implemented method of any one of Embodiments1-20, wherein the geometry of the one or more regions of plaquecomprises a round or oblong shape.

Embodiment 22: The computer-implemented method of any one of Embodiments1-21, wherein the one or more vascular morphology parameters comprises aclassification of arterial remodeling.

Embodiment 23: The computer-implemented method of Embodiment 22, whereinthe classification of arterial remodeling comprises positive arterialremodeling, negative arterial remodeling, and intermediate arterialremodeling.

Embodiment 24: The computer-implemented method of Embodiment 22, whereinthe classification of arterial remodeling is determined based at leastin part on a ratio of a largest vessel diameter at the one or moreregions of plaque to a normal reference vessel diameter.

Embodiment 25: The computer-implemented method of Embodiment 23, whereinthe classification of arterial remodeling comprises positive arterialremodeling, negative arterial remodeling, and intermediate arterialremodeling, and wherein positive arterial remodeling is determined whenthe ratio of the largest vessel diameter at the one or more regions ofplaque to the normal reference vessel diameter is more than 1.1, whereinnegative arterial remodeling is determined when the ratio of the largestvessel diameter at the one or more regions of plaque to the normalreference vessel diameter is less than 0.95, and wherein intermediatearterial remodeling is determined when the ratio of the largest vesseldiameter at the one or more regions of plaque to the normal referencevessel diameter is between 0.95 and 1.1.

Embodiment 26: The computer-implemented method of any one of Embodiments1-25, wherein the function of volume to surface area of the one or moreregions of plaque comprises one or more of a thickness or diameter ofthe one or more regions of plaque.

Embodiment 27: The computer-implemented method of any one of Embodiments1-26, wherein the weighted measure is generated by weighting the one ormore vascular morphology parameters and the set of quantified plaqueparameters of the one or more regions of plaque equally.

Embodiment 28: The computer-implemented method of any one of Embodiments1-26, wherein the weighted measure is generated by weighting the one ormore vascular morphology parameters and the set of quantified plaqueparameters of the one or more regions of plaque differently.

Embodiment 29: The computer-implemented method of any one of Embodiments1-26, wherein the weighted measure is generated by weighting the one ormore vascular morphology parameters and the set of quantified plaqueparameters of the one or more regions of plaque logarithmically,algebraically, or utilizing another mathematical transform.

Embodiment 30: A computer-implemented method of quantifying andclassifying vascular plaque based on non-invasive medical imageanalysis, the method comprising: accessing, by a computer system, amedical image of a subject, wherein the medical image of the subject isobtained non-invasively; identifying, by the computer system utilizingan artery identification algorithm, one or more arteries within themedical image of the subject, wherein the artery identificationalgorithm is configured to utilize raw medical images as input;identifying, by the computer system utilizing a plaque identificationalgorithm, one or more regions of plaque within the one or more arteriesidentified from the medical image of the subject, wherein the plaqueidentification algorithm is configured to utilize raw medical images asinput; determining, by the computer system, one or more vascularmorphology parameters and a set of quantified plaque parameters of theone or more identified regions of plaque from the medical image of thesubject, wherein the set of quantified plaque parameters comprises aratio or function of volume to surface area, heterogeneity index,geometry, and radiodensity of the one or more regions of plaque from themedical image; generating, by the computer system, a weighted measure ofthe determined one or more vascular morphology parameters and the set ofquantified plaque parameters of the one or more regions of plaque; andclassifying, by the computer system, the one or more regions of plaquewithin the medical image as stable plaque or unstable plaque based atleast in part on the generated weighted measure of the determined one ormore vascular morphology and the determined set of quantified plaqueparameters, wherein the computer system comprises a computer processorand an electronic storage medium.

Embodiment 31: The computer-implemented method of Embodiment 30, whereinthe identified one or more arteries comprise one or more of carotidarteries, aorta, renal artery, lower extremity artery, or cerebralartery.

Embodiment 32: A computer-implemented method of determiningnon-calcified plaque from a non-contrast Computed Tomography (CT) image,the method comprising: accessing, by a computer system, a non-contrastCT image of a coronary region of a subject; identifying, by the computersystem, epicardial fat on the non-contrast CT image; segmenting, by thecomputer system, arteries on the non-contrast CT image using theidentified epicardial fat as outer boundaries of the arteries;identifying, by the computer system, a first set of pixels within thearteries on the non-contrast CT image comprising a Hounsfield unitradiodensity value below a predetermined radiodensity threshold;classifying, by the computer system, the first set of pixels as a firstsubset of non-calcified plaque; identifying, by the computer system, asecond set of pixels within the arteries on the non-contrast CT imagecomprising a Hounsfield unit radiodensity value within a predeterminedradiodensity range; determining, by the computer system, a heterogeneityindex of the second set of pixels and identifying a subset of the secondset of pixels comprising a heterogeneity index above a heterogeneityindex threshold; classifying, by the computer system, the subset of thesecond set of pixels as a second subset of non-calcified plaque; anddetermining, by the computer system, non-calcified plaque from thenon-contrast CT image by combining the first subset of non-calcifiedplaque and the second subset of non-calcified plaque, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

Embodiment 33: The computer-implemented method of Embodiment 32, whereinthe predetermined radiodensity threshold comprises a Hounsfield unitradiodensity value of 30.

Embodiment 34: The computer-implemented method of any one of Embodiments32-33, wherein the predetermined radiodensity range comprises Hounsfieldunit radiodensity values between 30 and 100.

Embodiment 35: The computer-implemented method of any one of Embodiments32-34, wherein identifying epicardial fat on the non-contrast CT imagefurther comprises: determining a Hounsfield unit radiodensity value ofeach pixel within the non-contrast CT image; and classifying asepicardial fat pixels within the non-contrast CT image with a Hounsfieldunit radiodensity value within a predetermined epicardial fatradiodensity range, wherein the predetermined epicardial fatradiodensity range comprises a Hounsfield unit radiodensity value of-100.

Embodiment 36: The computer-implemented method of any one of Embodiments32-35, wherein the heterogeneity index of the second set of pixels isdetermined by generating spatial mapping of radiodensity values of thesecond set of pixels.

Embodiment 37: The computer-implemented method of any one of Embodiments32-36, wherein the heterogeneity index of the second set of pixels isdetermined by generating a three-dimensional histogram of radiodensityvalues across a geometric region within the second set of pixels.

Embodiment 38: The computer-implemented method of any one of Embodiments32-37, further comprising classifying, by the computer system, a subsetof the second set of pixels comprising a heterogeneity index below theheterogeneity index threshold as blood.

Embodiment 39: The computer-implemented method of any one of Embodiments32-38, further comprising generating a quantized color map of thecoronary region of the subject by assigning a first color to theidentified epicardial fat, assigning a second color to the segmentedarteries, and assigning a third color to the determined non-calcifiedplaque.

Embodiment 40: The computer-implemented method of any one of Embodiments32-39, further comprising: identifying, by the computer system, a thirdset of pixels within the arteries on the non-contrast CT imagecomprising a Hounsfield unit radiodensity value above a predeterminedcalcified radiodensity threshold; and classifying, by the computersystem, the third set of pixels as calcified plaque.

Embodiment 41: The computer-implemented method of any one of Embodiments32-40, further comprising determining, by the computer system, aproposed treatment based at least in part on the determinednon-calcified plaque.

Embodiment 42: A computer-implemented method of determininglow-attenuated plaque from a medical image of a subject, the methodcomprising: accessing, by a computer system, a medical image of asubject; identifying, by the computer system, epicardial fat on themedical image of the subject by: determining a radiodensity value ofeach pixel within the medical image of the subject; and classifying asepicardial fat pixels within the medical image of the subject with aradiodensity value within a predetermined epicardial fat radiodensityrange; segmenting, by the computer system, arteries on the medical imageof the subject using the identified epicardial fat as outer boundariesof the arteries; identifying, by the computer system, a first set ofpixels within the arteries on the medical image of the subjectcomprising a radiodensity value below a predetermined radiodensitythreshold; classifying, by the computer system, the first set of pixelsas a first subset of low-attenuated plaque; identifying, by the computersystem, a second set of pixels within the arteries on the non-contrastCT image comprising a radiodensity value within a predeterminedradiodensity range; determining, by the computer system, a heterogeneityindex of the second set of pixels and identifying a subset of the secondset of pixels comprising a heterogeneity index above a heterogeneityindex threshold; classifying, by the computer system, the subset of thesecond set of pixels as a second subset of low-attenuated plaque; anddetermining, by the computer system, low-attenuated plaque from themedical image of the subject by combining the first subset oflow-attenuated plaque and the second subset of low-attenuated plaque,wherein the computer system comprises a computer processor and anelectronic storage medium.

Embodiment 43: The computer-implemented method of Embodiment 42, whereinthe medical image comprises a Computed Tomography (CT) image.

Embodiment 44: The computer-implemented method of Embodiment 42, whereinthe medical image comprises a Magnetic Resonance (MR) image.

Embodiment 45: The computer-implemented method of Embodiment 42, whereinthe medical image comprises an ultrasound image.

Embodiment 46: The computer-implemented method of any one of Embodiments42-45, wherein the medical image comprises an image of a coronary regionof the subject.

Embodiment 47: The computer-implemented method of any one of Embodiments42-46, further comprising determining, by the computer system, aproposed treatment for a disease based at least in part on thedetermined low-attenuated plaque.

Embodiment 48: The computer-implemented method of Embodiment 47, whereinthe disease comprises one or more of arterial disease, renal arterydisease, abdominal atherosclerosis, or carotid atherosclerosis.

Embodiment 49: The computer-implemented method of any one of Embodiments42-48, wherein the heterogeneity index of the second set of pixels isdetermined by generating spatial mapping of radiodensity values of thesecond set of pixels.

Embodiment 50: A computer-implemented method of determiningnon-calcified plaque from a Dual-Energy Computed Tomography (DECT) imageor spectral Computed Tomography (CT) image, the method comprising:accessing, by a computer system, a DECT or spectral CT image of acoronary region of a subject; identifying, by the computer system,epicardial fat on the DECT image or spectral CT; segmenting, by thecomputer system, arteries on the DECT image or spectral CT; identifying,by the computer system, a first set of pixels within the arteries on theDECT or spectral CT image comprising a Hounsfield unit radiodensityvalue below a predetermined radiodensity threshold; classifying, by thecomputer system, the first set of pixels as a first subset ofnon-calcified plaque; identifying, by the computer system, a second setof pixels within the arteries on the DECT or spectral CT imagecomprising a Hounsfield unit radiodensity value within a predeterminedradiodensity range; classifying, by the computer system, a subset of thesecond set of pixels as a second subset of non-calcified plaque; anddetermining, by the computer system, non-calcified plaque from the DECTimage or spectral CT by combining the first subset of non-calcifiedplaque and the second subset of non-calcified plaque, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

Embodiment 51: The computer-implemented method of Embodiment 50, whereinthe subset of the second set of pixels is identified by determining, bythe computer system, a heterogeneity index of the second set of pixelsand identifying the subset of the second set of pixels comprising aheterogeneity index above a heterogeneity index threshold.

Embodiment 52: A computer-implemented method of assessing risk of acardiovascular event for a subject based on non-invasive medical imageanalysis, the method comprising: accessing, by a computer system, amedical image of a coronary region of a subject, wherein the medicalimage of the coronary region of the subject is obtained non-invasively;identifying, by the computer system utilizing a coronary arteryidentification algorithm, one or more coronary arteries within themedical image of the coronary region of the subject, wherein thecoronary artery identification algorithm is configured to utilize rawmedical images as input; identifying, by the computer system utilizing aplaque identification algorithm, one or more regions of plaque withinthe one or more coronary arteries identified from the medical image ofthe coronary region of the subject, wherein the plaque identificationalgorithm is configured to utilize raw medical images as input;determining, by the computer system, one or more vascular morphologyparameters and a set of quantified plaque parameters of the one or moreidentified regions of plaque from the medical image of the coronaryregion of the subject, wherein the set of quantified plaque parameterscomprises a ratio or function of volume to surface area, heterogeneityindex, geometry, and radiodensity of the one or more regions of plaquewithin the medical image; generating, by the computer system, a weightedmeasure of the determined one or more vascular morphology parameters andthe set of quantified plaque parameters of the one or more regions ofplaque; classifying, by the computer system, the one or more regions ofplaque within the medical image as stable plaque or unstable plaquebased at least in part on the generated weighted measure of thedetermined one or more vascular morphology parameters and the determinedset of quantified plaque parameters; generating, by the computer system,a risk of cardiovascular event for the subject based at least in part onthe one or more regions of plaque classified as stable plaque orunstable plaque; accessing, by the computer system, a coronary valuesdatabase comprising one or more known datasets of coronary valuesderived from one or more other subjects and comparing the one or moreregions of plaque classified as stable plaque or unstable plaque to theone or more known datasets of coronary values; updating, by the computersystem, the generated risk of cardiovascular event for the subject basedat least in part on the comparison of the one or more regions of plaqueclassified as stable plaque or unstable plaque to the one or more knowndatasets of coronary values; and generating, by the computer system, aproposed treatment for the subject based at least in part on thecomparison of the one or more regions of plaque classified as stableplaque or unstable plaque to the one or more known datasets of coronaryvalues, wherein the computer system comprises a computer processor andan electronic storage medium.

Embodiment 53: The computer-implemented method of Embodiment 52, whereinthe cardiovascular event comprises one or more of a Major AdverseCardiovascular Event (MACE), rapid plaque progression, or non-responseto medication.

Embodiment 54: The computer-implemented method of any one of Embodiments52-53, wherein the one or more known datasets of coronary valuescomprises one or more parameters of stable plaque and unstable plaquederived from medical images of healthy subjects.

Embodiment 55: The computer-implemented method of any one of Embodiments52-54, wherein the one or more other subjects are healthy.

Embodiment 56: The computer-implemented method of any one of Embodiments52-55, wherein the one or more other subjects have a heightened risk ofa cardiovascular event.

Embodiment 57: The computer-implemented method of any one of Embodiments52-57, further comprising: identifying, by the computer system, one ormore additional cardiovascular structures within the medical image,wherein the one or more additional cardiovascular structures compriseone or more of the left ventricle, right ventricle, left atrium, rightatrium, aortic valve, mitral valve, tricuspid valve, pulmonic valve,aorta, pulmonary artery, inferior and superior vena cava, epicardialfat, or pericardium; determining, by the computer system, one or moreparameters associated with the identified one or more additionalcardiovascular structures; classifying, by the computer system, the oneor more additional cardiovascular structures based at least in part onthe determined one or more parameters; accessing, by the computersystem, a cardiovascular structures values database comprising one ormore known datasets of cardiovascular structures parameters derived frommedical images of one or more other subjects and comparing theclassified one or more additional cardiovascular structures to the oneor more known datasets of cardiovascular structures parameters; andupdating, by the computer system, the generated risk of cardiovascularevent for the subject based at least in part on the comparison of theclassified one or more additional cardiovascular structures to the oneor more known datasets of cardiovascular structures parameters.

Embodiment 58: The computer-implemented method of Embodiment 57, whereinthe one or more additional cardiovascular structures are classified asnormal or abnormal.

Embodiment 59: The computer-implemented method of Embodiment 57, whereinthe one or more additional cardiovascular structures are classified asincreased or decreased.

Embodiment 60: The computer-implemented method of Embodiment 57, whereinthe one or more additional cardiovascular structures are classified asstatic or dynamic over time.

Embodiment 61: The computer-implemented method of any one of Embodiments57-60, further comprising generating, by the computer system, aquantized color map for the additional cardiovascular structures.

Embodiment 62: The computer-implemented method of any one of Embodiments57-61, further comprising updating, by the computer system, the proposedtreatment for the subject based at least in part on the comparison ofthe classified one or more additional cardiovascular structures to theone or more known datasets of cardiovascular structures parameters.

Embodiment 63: The computer-implemented method of any one of Embodiments57-62, further comprising: identifying, by the computer system, one ormore non-cardiovascular structures within the medical image, wherein theone or more non-cardiovascular structures comprise one or more of thelungs, bones, or liver; determining, by the computer system, one or moreparameters associated with the identified one or more non-cardiovascularstructures; classifying, by the computer system, the one or morenon-cardiovascular structures based at least in part on the determinedone or more parameters; accessing, by the computer system, anon-cardiovascular structures values database comprising one or moreknown datasets of non-cardiovascular structures parameters derived frommedical images of one or more other subjects and comparing theclassified one or more non-cardiovascular structures to the one or moreknown datasets of non-cardiovascular structures parameters; andupdating, by the computer system, the generated risk of cardiovascularevent for the subject based at least in part on the comparison of theclassified one or more non-cardiovascular structures to the one or moreknown datasets of non-cardiovascular structures parameters.

Embodiment 64: The computer-implemented method of Embodiment 63, whereinthe one or more non-cardiovascular structures are classified as normalor abnormal.

Embodiment 65: The computer-implemented method of Embodiment 63, whereinthe one or more non-cardiovascular structures are classified asincreased or decreased.

Embodiment 66: The computer-implemented method of Embodiment 63, whereinthe one or more non-cardiovascular structures are classified as staticor dynamic over time.

Embodiment 67: The computer-implemented method of any one of Embodiments63-66, further comprising generating, by the computer system, aquantized color map for the non-cardiovascular structures.

Embodiment 68: The computer-implemented method of any one of Embodiments63-67, further comprising updating, by the computer system, the proposedtreatment for the subject based at least in part on the comparison ofthe classified one or more non-cardiovascular structures to the one ormore known datasets of non-cardiovascular structures parameters.

Embodiment 69: The computer-implemented method of any one of Embodiments63-68, wherein the one or more parameters associated with the identifiedone or more non-cardiovascular structures comprises one or more of ratioof volume to surface area, heterogeneity, radiodensity, or geometry ofthe identified one or more non-cardiovascular structures.

Embodiment 70: The computer-implemented method of any one of Embodiments52-69, wherein the medical image comprises a Computed Tomography (CT)image.

Embodiment 71: The computer-implemented method of any one of Embodiments52-69, wherein the medical image comprises a Magnetic Resonance (MR)image.

Embodiment 72: A computer-implemented method of quantifying andclassifying coronary atherosclerosis within a coronary region of asubject based on non-invasive medical image analysis, the methodcomprising: accessing, by a computer system, a medical image of acoronary region of a subject, wherein the medical image of the coronaryregion of the subject is obtained non-invasively; identifying, by thecomputer system utilizing a coronary artery identification algorithm,one or more coronary arteries within the medical image of the coronaryregion of the subject, wherein the coronary artery identificationalgorithm is configured to utilize raw medical images as input;identifying, by the computer system utilizing a plaque identificationalgorithm, one or more regions of plaque within the one or more coronaryarteries identified from the medical image of the coronary region of thesubject, wherein the plaque identification algorithm is configured toutilize raw medical images as input; determining, by the computersystem, one or more vascular morphology parameters and a set ofquantified plaque parameters of the one or more identified regions ofplaque from the medical image of the coronary region of the subject,wherein the set of quantified plaque parameters comprises a ratio orfunction of volume to surface area, heterogeneity index, geometry, andradiodensity of the one or more regions of plaque within the medicalimage; generating, by the computer system, a weighted measure of thedetermined one or more vascular morphology parameters and the set ofquantified plaque parameters of the one or more regions of plaque;quantifying, by the computer system, coronary atherosclerosis of thesubject based at least in part on the set of generated weighted measureof the determined one or more vascular morphology parameters and thedetermined quantified plaque parameters; and classifying, by thecomputer system, coronary atherosclerosis of the subject as one or moreof high risk, medium risk, or low risk based at least in part on thequantified coronary atherosclerosis of the subject, wherein the computersystem comprises a computer processor and an electronic storage medium.

Embodiment 73: The computer-implemented method of Embodiment 72, whereinone or more of the coronary artery identification algorithm or theplaque identification algorithm comprises an artificial intelligence ormachine learning algorithm.

Embodiment 74: The computer-implemented method of any one of Embodiments72 or 73, further comprising determining a numerical calculation ofcoronary stenosis of the subject based at least in part on the one ormore vascular morphology parameters and/or set of quantified plaqueparameters determined from the medical image of the coronary region ofthe subject.

Embodiment 75: The computer-implemented method of any one of Embodiment72-74, further comprising assessing a risk of ischemia for the subjectbased at least in part on the one or more vascular morphology parametersand/or set of quantified plaque parameters determined from the medicalimage of the coronary region of the subject.

Embodiment 76: The computer-implemented method of any one of Embodiments72-75, wherein the plaque identification algorithm is configured todetermine the one or more regions of plaque by determining a vessel walland lumen wall of the one or more coronary arteries and determining avolume between the vessel wall and lumen wall as the one or more regionsof plaque.

Embodiment 77: The computer-implemented method of any one of Embodiments72-76, wherein the one or more coronary arteries are identified by size.

Embodiment 78: The computer-implemented method of any one of Embodiments72-77, wherein a ratio of volume to surface area of the one or moreregions of plaque below a predetermined threshold is indicative of lowrisk.

Embodiment 79: The computer-implemented method of any one of Embodiments72-78, wherein a radiodensity of the one or more regions of plaque abovea predetermined threshold is indicative of low risk.

Embodiment 80: The computer-implemented method of any one of Embodiments72-79, wherein a heterogeneity of the one or more regions of plaquebelow a predetermined threshold is indicative of low risk.

Embodiment 81: The computer-implemented method of any one of Embodiments72-80, wherein the set of quantified plaque parameters further comprisesdiffusivity of the one or more regions of plaque.

Embodiment 82: The computer-implemented method of any one of Embodiments72-81, wherein the set of quantified plaque parameters further comprisesa ratio of radiodensity to volume of the one or more regions of plaque.

Embodiment 83: The computer-implemented method of any one of Embodiments72-82, further comprising generating, by the computer system, a proposedtreatment for the subject based at least in part on the classifiedatherosclerosis.

Embodiment 84: The computer-implemented method of any one of Embodiments72-83, wherein the coronary atherosclerosis of the subject is classifiedby the computer system using a coronary atherosclerosis classificationalgorithm, wherein the coronary atherosclerosis classification algorithmis configured to utilize a combination of the ratio of volume of surfacearea, volume, heterogeneity index, and radiodensity of the one or moreregions of plaque as input.

Embodiment 85: The computer-implemented method of any one of Embodiments72-84, wherein the medical image comprises a Computed Tomography (CT)image.

Embodiment 86: The computer-implemented method of Embodiment 85, whereinthe medical image comprises a non-contrast CT image.

Embodiment 87: The computer-implemented method of Embodiment 85, whereinthe medical image comprises a contrast CT image.

Embodiment 88: The computer-implemented method of any one of Embodiments72-84, wherein the medical image is obtained using an imaging techniquecomprising one or more of CT, x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), MR imaging, optical coherencetomography (OCT), nuclear medicine imaging, positron-emission tomography(PET), single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).

Embodiment 89: The computer-implemented method of any one of Embodiments72-88, wherein the heterogeneity index of one or more regions of plaqueis determined by generating a three-dimensional histogram ofradiodensity values across a geometric shape of the one or more regionsof plaque.

Embodiment 90: The computer-implemented method of any one of Embodiments72-89, wherein the heterogeneity index of one or more regions of plaqueis determined by generating spatial mapping of radiodensity valuesacross the one or more regions of plaque.

Embodiment 91: The computer-implemented method of any one of Embodiments72-90, wherein the set of quantified plaque parameters comprises apercentage composition of plaque comprising different radiodensityvalues.

Embodiment 92: The computer-implemented method of any one of Embodiments72-91, wherein the set of quantified plaque parameters comprises apercentage composition of plaque comprising different radiodensityvalues as a function of volume of plaque.

Embodiment 93: The computer-implemented method of any one of Embodiments72-92, wherein the weighted measure of the determined one or morevascular morphology parameters and the set of quantified plaqueparameters of the one or more regions of plaque is generated based atleast in part by comparing the determined set of quantified plaqueparameters to one or more predetermined sets of quantified plaqueparameters.

Embodiment 94: The computer-implemented method of Embodiment 93, whereinthe one or more predetermined sets of quantified plaque parameters arederived from one or more medical images of other subjects.

Embodiment 95: The computer-implemented method of Embodiment 93, whereinthe one or more predetermined sets of quantified plaque parameters arederived from one or more medical images of the subject.

Embodiment 96: The computer-implemented method of any one of Embodiments72-95, wherein the geometry of the one or more regions of plaquecomprises a round or oblong shape.

Embodiment 97: The computer-implemented method of any one of Embodiments72-96, wherein the one or more vascular morphology parameters comprisesa classification of arterial remodeling.

Embodiment 98: The computer-implemented method of Embodiment 97, whereinthe classification of arterial remodeling comprises positive arterialremodeling, negative arterial remodeling, and intermediate arterialremodeling.

Embodiment 99: The computer-implemented method of Embodiment 97, whereinthe classification of arterial remodeling is determined based at leastin part on a ratio of a largest vessel diameter at the one or moreregions of plaque to a normal reference vessel diameter.

Embodiment 100: The computer-implemented method of Embodiment 99,wherein the classification of arterial remodeling comprises positivearterial remodeling, negative arterial remodeling, and intermediatearterial remodeling, and wherein positive arterial remodeling isdetermined when the ratio of the largest vessel diameter at the one ormore regions of plaque to the normal reference vessel diameter is morethan 1.1, wherein negative arterial remodeling is determined when theratio of the largest vessel diameter at the one or more regions ofplaque to the normal reference vessel diameter is less than 0.95, andwherein intermediate arterial remodeling is determined when the ratio ofthe largest vessel diameter at the one or more regions of plaque to thenormal reference vessel diameter is between 0.95 and 1.1.

Embodiment 101: The computer-implemented method of any one ofEmbodiments 72-100, wherein the function of volume to surface area ofthe one or more regions of plaque comprises one or more of a thicknessor diameter of the one or more regions of plaque.

Embodiment 102: The computer-implemented method of any one ofEmbodiments 72-101, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters and the set ofquantified plaque parameters of the one or more regions of plaqueequally.

Embodiment 103: The computer-implemented method of any one ofEmbodiments 72-101, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters and the set ofquantified plaque parameters of the one or more regions of plaquedifferently.

Embodiment 104: The computer-implemented method of any one ofEmbodiments 72-101, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters and the set ofquantified plaque parameters of the one or more regions of plaquelogarithmically, algebraically, or utilizing another mathematicaltransform.

Embodiment 105: A computer-implemented method of quantifying a state ofcoronary artery disease based on quantification of plaque, ischemia, andfat inflammation based on non-invasive medical image analysis, themethod comprising: accessing, by a computer system, a medical image of acoronary region of a subject, wherein the medical image of the coronaryregion of the subject is obtained non-invasively; identifying, by thecomputer system utilizing a coronary artery identification algorithm,one or more coronary arteries within the medical image of the coronaryregion of the subject, wherein the coronary artery identificationalgorithm is configured to utilize raw medical images as input;identifying, by the computer system utilizing a plaque identificationalgorithm, one or more regions of plaque within the one or more coronaryarteries identified from the medical image of the coronary region of thesubject, wherein the plaque identification algorithm is configured toutilize raw medical images as input; identifying, by the computer systemutilizing a fat identification algorithm, one or more regions of fatwithin the medical image of the coronary region of the subject, whereinthe fat identification algorithm is configured to utilize raw medicalimages as input; determining, by the computer system, one or morevascular morphology parameters and a set of quantified plaque parametersof the one or more identified regions of plaque from the medical imageof the coronary region of the subject, wherein the set of quantifiedplaque parameters comprises a ratio or function of volume to surfacearea, heterogeneity index, geometry, and radiodensity of the one or moreregions of plaque within the medical image; quantifying, by the computersystem, coronary stenosis based at least in part on the set ofquantified plaque parameters determined from the medical image of thecoronary region of the subject; and determining, by the computer system,a presence or risk of ischemia based at least in part on the set ofquantified plaque parameters determined from the medical image of thecoronary region of the subject; determining, by the computer system, aset of quantified fat parameters of the one or more identified regionsof fat within the medical image of the coronary region of the subject,wherein the set of quantified fat parameters comprises volume, geometry,and radiodensity of the one or more regions of fat within the medicalimage; generating, by the computer system, a weighted measure of thedetermined one or more vascular morphology parameters, the set ofquantified plaque parameters of the one or more regions of plaque, thequantified coronary stenosis, the determined presence or risk ofischemia, and the determined set of quantified fat parameters; andgenerating, by the computer system, a risk assessment of coronarydisease of the subject based at least in part on the generated weightedmeasure of the determined one or more vascular morphology parameters,the set of quantified plaque parameters of the one or more regions ofplaque, the quantified coronary stenosis, the determined presence orrisk of ischemia, and the determined set of quantified fat parameters,wherein the computer system comprises a computer processor and anelectronic storage medium.

Embodiment 106: The computer-implemented method of Embodiment 105,wherein one or more of the coronary artery identification algorithm,plaque identification algorithm, or fat identification algorithmcomprises an artificial intelligence or machine learning algorithm.

Embodiment 107: The computer-implemented method of any one of Embodiment105 or 106, further comprising automatically generating, by the computersystem, a Coronary Artery Disease Reporting & Data System (CAD-RADS)classification score of the subject based at least in part on thequantified coronary stenosis.

Embodiment 108: The computer-implemented method of any one ofEmbodiments 105-107, further comprising automatically generating, by thecomputer system, a CAD-RADS modifier of the subject based at least inpart on one or more of the determined one or more vascular morphologyparameters, the set of quantified plaque parameters of the one or moreregions of plaque, the quantified coronary stenosis, the determinedpresence or risk of ischemia, and the determined set of quantified fatparameters, wherein the CAD-RADS modifier comprises one or more ofnondiagnostic (N), stent (S), graft (G), or vulnerability (V).

Embodiment 109: The computer-implemented method of any one ofEmbodiments 105-108, wherein the coronary stenosis is quantified on avessel-by-vessel basis.

Embodiment 110: The computer-implemented method of any one ofEmbodiments 105-109, wherein the presence or risk of ischemia isdetermined on a vessel-by-vessel basis.

Embodiment 111: The computer-implemented method of any one ofEmbodiments 105-110, wherein the one or more regions of fat comprisesepicardial fat.

Embodiment 112: The computer-implemented method of any one ofEmbodiments 105-111, further comprising generating, by the computersystem, a proposed treatment for the subject based at least in part onthe generated risk assessment of coronary disease.

Embodiment 113: The computer-implemented method of any one ofEmbodiments 105-112, wherein the medical image comprises a ComputedTomography (CT) image.

Embodiment 114: The computer-implemented method of Embodiment 113,wherein the medical image comprises a non-contrast CT image.

Embodiment 115: The computer-implemented method of Embodiment 113,wherein the medical image comprises a contrast CT image.

Embodiment 116: The computer-implemented method of any one ofEmbodiments 113-115, wherein the determined set of plaque parameterscomprises one or more of a percentage of higher radiodensity calciumplaque or lower radiodensity calcium plaque within the one or moreregions of plaque, wherein higher radiodensity calcium plaque comprisesa Hounsfield radiodensity unit of above 1000, and wherein lowerradiodensity calcium plaque comprises a Hounsfield radiodensity unit ofbelow 1000.

Embodiment 117: The computer-implemented method of any one ofEmbodiments 105-112, wherein the medical image comprises a MagneticResonance (MR) image.

Embodiment 118: The computer-implemented method of any one ofEmbodiments 105-112, wherein the medical image comprises an ultrasoundimage.

Embodiment 119: The computer-implemented method of any one ofEmbodiments 105-112, wherein the medical image is obtained using animaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 120: The computer-implemented method of any one ofEmbodiments 105-119, wherein the heterogeneity index of one or moreregions of plaque is determined by generating a three-dimensionalhistogram of radiodensity values across a geometric shape of the one ormore regions of plaque.

Embodiment 121: The computer-implemented method of any one ofEmbodiments 105-119, wherein the heterogeneity index of one or moreregions of plaque is determined by generating spatial mapping ofradiodensity values across the one or more regions of plaque.

Embodiment 122: The computer-implemented method of any one ofEmbodiments 105-121, wherein the set of quantified plaque parameterscomprises a percentage composition of plaque comprising differentradiodensity values.

Embodiment 123: The computer-implemented method of any one ofEmbodiments 105-122, wherein the set of quantified plaque parametersfurther comprises diffusivity of the one or more regions of plaque.

Embodiment 124: The computer-implemented method of any one ofEmbodiments 105-123, wherein the set of quantified plaque parametersfurther comprises a ratio of radiodensity to volume of the one or moreregions of plaque.

Embodiment 125: The computer-implemented method of any one ofEmbodiments 105-124, wherein the plaque identification algorithm isconfigured to determine the one or more regions of plaque by determininga vessel wall and lumen wall of the one or more coronary arteries anddetermining a volume between the vessel wall and lumen wall as the oneor more regions of plaque.

Embodiment 126: The computer-implemented method of any one ofEmbodiments 105-125, wherein the one or more coronary arteries areidentified by size.

Embodiment 127: The computer-implemented method of any one ofEmbodiments 105-126, wherein the generated risk assessment of coronarydisease of the subject comprises a risk score.

Embodiment 128: The computer-implemented method of any one ofEmbodiments 105-127, wherein the geometry of the one or more regions ofplaque comprises a round or oblong shape.

Embodiment 129: The computer-implemented method of any one ofEmbodiments 105-128, wherein the one or more vascular morphologyparameters comprises a classification of arterial remodeling.

Embodiment 130: The computer-implemented method of Embodiment 129,wherein the classification of arterial remodeling comprises positivearterial remodeling, negative arterial remodeling, and intermediatearterial remodeling.

Embodiment 131: The computer-implemented method of Embodiment 129,wherein the classification of arterial remodeling is determined based atleast in part on a ratio of a largest vessel diameter at the one or moreregions of plaque to a normal reference vessel diameter.

Embodiment 132: The computer-implemented method of Embodiment 131,wherein the classification of arterial remodeling comprises positivearterial remodeling, negative arterial remodeling, and intermediatearterial remodeling, and wherein positive arterial remodeling isdetermined when the ratio of the largest vessel diameter at the one ormore regions of plaque to the normal reference vessel diameter is morethan 1.1, wherein negative arterial remodeling is determined when theratio of the largest vessel diameter at the one or more regions ofplaque to the normal reference vessel diameter is less than 0.95, andwherein intermediate arterial remodeling is determined when the ratio ofthe largest vessel diameter at the one or more regions of plaque to thenormal reference vessel diameter is between 0.95 and 1.1.

Embodiment 133: The computer-implemented method of any of Embodiments105-132, wherein the function of volume to surface area of the one ormore regions of plaque comprises one or more of a thickness or diameterof the one or more regions of plaque.

Embodiment 134: The computer-implemented method of any one ofEmbodiments 105-133, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters, the set ofquantified plaque parameters of the one or more regions of plaque, thequantified coronary stenosis, the determined presence or risk ofischemia, and the determined set of quantified fat parameters equally.

Embodiment 135: The computer-implemented method of any one ofEmbodiments 105-133, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters, the set ofquantified plaque parameters of the one or more regions of plaque, thequantified coronary stenosis, the determined presence or risk ofischemia, and the determined set of quantified fat parametersdifferently.

Embodiment 136: The computer-implemented method of any one ofEmbodiments 105-133, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters, the set ofquantified plaque parameters of the one or more regions of plaque, thequantified coronary stenosis, the determined presence or risk ofischemia, and the determined set of quantified fat parameterslogarithmically, algebraically, or utilizing another mathematicaltransform.

Embodiment 137: A computer-implemented method of tracking a plaque-baseddisease based at least in part on determining a state of plaqueprogression of a subject using non-invasive medical image analysis, themethod comprising: accessing, by a computer system, a first set ofplaque parameters associated with a region of a subject, wherein thefirst set of plaque parameters are derived from a first medical image ofthe subject, wherein the first medical image of the subject is obtainednon-invasively at a first point in time; accessing, by a computersystem, a second medical image of the subject, wherein the secondmedical image of the subject is obtained non-invasively at a secondpoint in time, the second point in time being later than the first pointin time; identifying, by the computer system, one or more regions ofplaque from the second medical image; determining, by the computersystem, a second set of plaque parameters associated with the region ofthe subject by analyzing the second medical image and the identified oneor more regions of plaque from the second medical image; analyzing, bythe computer system, a change in one or more plaque parameters bycomparing one or more of the first set of plaque parameters against oneor more of the second set of plaque parameters; determining, by thecomputer system, a state of plaque progression associated with aplaque-based disease for the subject based at least in part on theanalyzed change in the one or more plaque parameters, wherein thedetermined state of plaque progression comprises one or more of rapidplaque progression, non-rapid calcium dominant mixed response, non-rapidnon-calcium dominant mixed response, or plaque regression; and tracking,by the computer system, progression of the plaque-based disease based atleast in part on the determined state of plaque progression, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

Embodiment 138: The computer-implemented method of Embodiment 137,wherein rapid plaque progression is determined when a percent atheromavolume increase of the subject is more than 1% per year, whereinnon-rapid calcium dominant mixed response is determined when a percentatheroma volume increase of the subject is less than 1% per year andcalcified plaque represents more than 50% of total new plaque formation,wherein non-rapid non-calcium dominant mixed response is determined whena percent atheroma volume increase of the subject is less than 1% peryear and non-calcified plaque represents more than 50% of total newplaque formation, and wherein plaque regression is determined when adecrease in total percent atheroma volume is present.

Embodiment 139: The computer-implemented method of any one ofEmbodiments 137-138, further comprising generating, by the computersystem, a proposed treatment for the subject based at least in part onthe determined state of plaque progression of the plaque-based disease.

Embodiment 140: The computer-implemented method of any one ofEmbodiments 137-139, wherein the medical image comprises a ComputedTomography (CT) image.

Embodiment 141: The computer-implemented method of Embodiment 140,wherein the medical image comprises a non-contrast CT image.

Embodiment 142: The computer-implemented method of Embodiment 140,wherein the medical image comprises a contrast CT image.

Embodiment 143: The computer-implemented method of any one ofEmbodiments 140-142, wherein the determined state of plaque progressionfurther comprises one or more of a percentage of higher radiodensityplaques or lower radiodensity plaques, wherein higher radiodensityplaques comprise a Hounsfield unit of above 1000, and wherein lowerradiodensity plaques comprise a Hounsfield unit of below 1000.

Embodiment 144: The computer-implemented method of any one ofEmbodiments 137-139, wherein the medical image comprises a MagneticResonance (MR) image.

Embodiment 145: The computer-implemented method of any one ofEmbodiments 137-139, wherein the medical image comprises an ultrasoundimage.

Embodiment 146: The computer-implemented method of any one ofEmbodiments 137-145, wherein the region of the subject comprises acoronary region of the subject.

Embodiment 147: The computer-implemented method of any one ofEmbodiments 137-145, wherein the region of the subject comprises one ormore of carotid arteries, renal arteries, abdominal aorta, cerebralarteries, lower extremities, or upper extremities.

Embodiment 148: The computer-implemented method of any one ofEmbodiments 137-147, wherein the plaque-based disease comprises one ormore of atherosclerosis, stenosis, or ischemia.

Embodiment 149: The computer-implemented method of any one ofEmbodiments 137-148, further comprising: determining, by the computersystem, a first Coronary Artery Disease Reporting & Data System(CAD-RADS) classification score of the subject based at least in part onthe first set of plaque parameters; determining, by the computer system,a second CAD-RADS classification score of the subject based at least inpart on the second set of plaque parameters; and tracking, by thecomputer system, progression of a CAD-RADS classification score of thesubject based on comparing the first CAD-RADS classification score andthe second CAD-RADS classification score.

Embodiment 150: The computer-implemented method of any one ofEmbodiments 137-149, wherein the plaque-based disease is further trackedby the computer system by analyzing one or more of serum biomarkers,genetics, omics, transcriptomics, microbiomics, or metabolomics.

Embodiment 151: The computer-implemented method of any one ofEmbodiments 137-150, wherein the first set of plaque parameterscomprises one or more of a volume, surface area, geometric shape,location, heterogeneity index, and radiodensity of one or more regionsof plaque within the first medical image.

Embodiment 152: The computer-implemented method of any one ofEmbodiments 137-151, wherein the second set of plaque parameterscomprises one or more of a volume, surface area, geometric shape,location, heterogeneity index, and radiodensity of one or more regionsof plaque within the second medical image.

Embodiment 153: The computer-implemented method of any one ofEmbodiments 137-152, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a ratio of radiodensity tovolume of one or more regions of plaque.

Embodiment 154: The computer-implemented method of any one ofEmbodiments 137-153, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a diffusivity of one or moreregions of plaque.

Embodiment 155: The computer-implemented method of any one ofEmbodiments 137-154, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a volume to surface area ratioof one or more regions of plaque.

Embodiment 156: The computer-implemented method of any one ofEmbodiments 137-155, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a heterogeneity index of one ormore regions of plaque.

Embodiment 157: The computer-implemented method of Embodiment 156,wherein the heterogeneity index of one or more regions of plaque isdetermined by generating a three-dimensional histogram of radiodensityvalues across a geometric shape of the one or more regions of plaque.

Embodiment 158: The computer-implemented method of Embodiment 156,wherein the heterogeneity index of one or more regions of plaque isdetermined by generating spatial mapping of radiodensity values acrossthe one or more regions of plaque.

Embodiment 159: The computer-implemented method of any one ofEmbodiments 137-158, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a percentage composition ofplaque comprising different radiodensity values.

Embodiment 160: The computer-implemented method of any one ofEmbodiments 137-159, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a percentage composition ofplaque comprising different radiodensity values as a function of volumeof plaque.

Embodiment 161: A computer-implemented method of characterizing a changein coronary calcium score of a subject, the method comprising:accessing, by the computer system, a first coronary calcium score of asubject and a first set of plaque parameters associated with a coronaryregion of a subject, the first coronary calcium score and the first setof parameters obtained at a first point in time, wherein the first setof plaque parameters comprises volume, surface area, geometric shape,location, heterogeneity index, and radiodensity for one or more regionsof plaque within the coronary region of the subject; generating, by thecomputer system, a first weighted measure of the accessed first set ofplaque parameters; accessing, by a computer system, a second coronarycalcium score of the subject and one or more medical images of thecoronary region of the subject, the second coronary calcium score andthe one or more medical images obtained at a second point in time, thesecond point in time being later than the first point in time, whereinthe one or more medical images of the coronary region of the subjectcomprises the one or more regions of plaque; determining, by thecomputer system, a change in coronary calcium score of the subject bycomparing the first coronary calcium score and the second coronarycalcium score; identifying, by the computer system, the one or moreregions of plaque from the one or more medical images; determining, bythe computer system, a second set of plaque parameters associated withthe coronary region of the subject by analyzing the one or more medicalimages, wherein the second set of plaque parameters comprises volume,surface area, geometric shape, location, heterogeneity index, andradiodensity for the one or more regions of plaque; generating, by thecomputer system, a second weighted measure of the determined second setof plaque parameters; analyzing, by the computer system, a change in thefirst weighted measure of the accessed first set of plaque parametersand the second weighted measure of the determined second set of plaqueparameters; and characterizing, by the computer system, the change incoronary calcium score of the subject based at least in part on theidentified one or more regions of plaque and the analyzed change in thefirst weighted measure of the accessed first set of plaque parametersand the second weighted measure of the determined second set of plaqueparameters, wherein the change in coronary calcium score ischaracterized as positive, neutral, or negative, wherein the computersystem comprises a computer processor and an electronic storage medium.

Embodiment 162: The computer-implemented method of Embodiment 161,wherein radiodensity of the one or more regions of plaque is determinedfrom the one or more medical images by analyzing a Hounsfield unit ofthe identified one or more regions of plaque.

Embodiment 163: The computer-implemented method of any one ofEmbodiments 161-162, further comprising determining a change in ratiobetween volume and radiodensity of the one or more regions of plaquewithin the coronary region of the subject, and wherein the change incoronary calcium score of the subject is further characterized based atleast in part the determined change in ratio between volume andradiodensity of one or more regions of plaque within the coronary regionof the subject.

Embodiment 164: The computer-implemented method of any one ofEmbodiments 161-163, wherein the change in coronary calcium score of thesubject is characterized for each vessel.

Embodiment 165: The computer-implemented method of any one ofEmbodiments 161-164, wherein the change in coronary calcium score of thesubject is characterized for each segment.

Embodiment 166: The computer-implemented method of any one ofEmbodiments 161-165, wherein the change in coronary calcium score of thesubject is characterized for each plaque.

Embodiment 167: The computer-implemented method of any one ofEmbodiments 161-166, wherein the first set of plaque parameters and thesecond set of plaque parameters further comprise a diffusivity of theone or more regions of plaque.

Embodiment 168: The computer-implemented method of any one ofEmbodiments 161-167, wherein the change in coronary calcium score of thesubject is characterized as positive when the radiodensity of the one ormore regions of plaque is increased.

Embodiment 169: The computer-implemented method of any one ofEmbodiments 161-168, wherein the change in coronary calcium score of thesubject is characterized as negative when one or more new regions ofplaque are identified from the one or more medical images.

Embodiment 170: The computer-implemented method of any one ofEmbodiments 161-169, wherein the change in coronary calcium score of thesubject is characterized as positive when a volume to surface area ratioof the one or more regions of plaque is decreased.

Embodiment 171: The computer-implemented method of any one ofEmbodiments 161-170, wherein the heterogeneity index of the one or moreregions of plaque is determined by generating a three-dimensionalhistogram of radiodensity values across a geometric shape of the one ormore regions of plaque.

Embodiment 172: The computer-implemented method of any one ofEmbodiments 161-171, wherein the change in coronary calcium score of thesubject is characterized as positive when the heterogeneity index of theone or more regions of plaque is decreased.

Embodiment 173: The computer-implemented method of any one ofEmbodiments 161-172, wherein the second coronary calcium score of thesubject is determined by analyzing the one or more medical images of thecoronary region of the subject.

Embodiment 174: The computer-implemented method of any one ofEmbodiments 161-172, wherein the second coronary calcium score of thesubject is accessed from a database.

Embodiment 175: The computer-implemented method of any one ofEmbodiments 161-174, wherein the one or more medical images of thecoronary region of the subject comprises an image obtained from anon-contrast Computed Tomography (CT) scan.

Embodiment 176: The computer-implemented method of any one ofEmbodiments 161-174, wherein the one or more medical images of thecoronary region of the subject comprises an image obtained from acontrast-enhanced CT scan.

Embodiment 177: The computer-implemented method of Embodiment 176,wherein the one or more medical images of the coronary region of thesubject comprises an image obtained from a contrast-enhanced CTangiogram.

Embodiment 178: The computer-implemented method of any one ofEmbodiments 161-177, wherein a positive characterization of the changein coronary calcium score is indicative of plaque stabilization.

Embodiment 179: The computer-implemented method of any one ofEmbodiments 161-178, wherein the first set of plaque parameters and thesecond set of plaque parameters further comprise radiodensity of avolume around plaque.

Embodiment 180: The computer-implemented method of any one ofEmbodiments 161-179, wherein the change in coronary calcium score of thesubject is characterized by a machine learning algorithm utilized by thecomputer system.

Embodiment 181: The computer-implemented method of any one ofEmbodiments 161-180, wherein the first weighted measure is generated byweighting the accessed first set of plaque parameters equally.

Embodiment 182: The computer-implemented method of any one ofEmbodiments 161-180, wherein the first weighted measure is generated byweighting the accessed first set of plaque parameters differently.

Embodiment 183: The computer-implemented method of any one ofEmbodiments 161-180, wherein the first weighted measure is generated byweighting the accessed first set of plaque parameters logarithmically,algebraically, or utilizing another mathematical transform.

Embodiment 184: A computer-implemented method of generating prognosis ofa cardiovascular event for a subject based on non-invasive medical imageanalysis, the method comprising: accessing, by a computer system, amedical image of a coronary region of a subject, wherein the medicalimage of the coronary region of the subject is obtained non-invasively;identifying, by the computer system utilizing a coronary arteryidentification algorithm, one or more coronary arteries within themedical image of the coronary region of the subject, wherein thecoronary artery identification algorithm is configured to utilize rawmedical images as input; identifying, by the computer system utilizing aplaque identification algorithm, one or more regions of plaque withinthe one or more coronary arteries identified from the medical image ofthe coronary region of the subject, wherein the plaque identificationalgorithm is configured to utilize raw medical images as input;determining, by the computer system, a set of quantified plaqueparameters of the one or more identified regions of plaque within themedical image of the coronary region of the subject, wherein the set ofquantified plaque parameters comprises volume, surface area, ratio ofvolume to surface area, heterogeneity index, geometry, and radiodensityof the one or more regions of plaque within the medical image;classifying, by the computer system, the one or more regions of plaquewithin the medical image as stable plaque or unstable plaque based atleast in part on the determined set of quantified plaque parameters;determining, by the computer system, a volume of unstable plaqueclassified within the medical image and a total volume of the one ormore coronary arteries within the medical image; determining, by thecomputer system, a ratio of volume of unstable plaque to the totalvolume of the one or more coronary arteries; generating, by the computersystem, a prognosis of a cardiovascular event for the subject based atleast in part on analyzing the ratio of volume of unstable plaque to thetotal volume of the one or more coronary arteries, the volume of the oneor more regions of plaque, and the volume of unstable plaque classifiedwithin the medical image, wherein the analyzing comprises conducting acomparison to a known dataset of one or more ratios of volume ofunstable plaque to total volume of one or more coronary arteries, volumeof one or more regions of plaque, and volume of unstable plaque, whereinthe known dataset is collected from other subjects; and generating, bythe computer system, treatment plan for the subject based at least inpart on the generated prognosis of cardiovascular event for the subject,wherein the computer system comprises a computer processor and anelectronic storage medium.

Embodiment 185: The computer-implemented method of Embodiment 184,further comprising generating, by the computer system, a weightedmeasure of the ratio of volume of unstable plaque to the total volume ofthe one or more coronary arteries, the volume of the one or more regionsof plaque, and the volume of unstable plaque classified within themedical image, wherein the prognosis of cardiovascular event is furthergenerated by comparing the weighted measure to one or more weightedmeasures derived from the known dataset.

Embodiment 186: The computer-implemented method of Embodiment 185,wherein the weighted measure is generated by weighting the ratio ofvolume of unstable plaque to the total volume of the one or morecoronary arteries, the volume of the one or more regions of plaque, andthe volume of unstable plaque classified within the medical imageequally.

Embodiment 187: The computer-implemented method of Embodiment 185,wherein the weighted measure is generated by weighting the ratio ofvolume of unstable plaque to the total volume of the one or morecoronary arteries, the volume of the one or more regions of plaque, andthe volume of unstable plaque classified within the medical imagedifferently.

Embodiment 188: The computer-implemented method of Embodiment 185,wherein the weighted measure is generated by weighting the ratio ofvolume of unstable plaque to the total volume of the one or morecoronary arteries, the volume of the one or more regions of plaque, andthe volume of unstable plaque classified within the medical imagelogarithmically, algebraically, or utilizing another mathematicaltransform.

Embodiment 189: The computer-implemented method of any one ofEmbodiments 184-188, further comprising analyzing, by the computersystem, a medical image of a non-coronary cardiovascular system of thesubject, and wherein the prognosis of a cardiovascular event for thesubject is further generated based at least in part on the analyzedmedical image of the non-coronary cardiovascular system of the subject.

Embodiment 190: The computer-implemented method of any one ofEmbodiments 184-189, further comprising accessing, by the computersystem, results of a blood chemistry or biomarker test of the subject,and wherein the prognosis of a cardiovascular event for the subject isfurther generated based at least in part on the results of the bloodchemistry or biomarker test of the subject.

Embodiment 191: The computer-implemented method of any one ofEmbodiments 184-190, wherein the generated prognosis of a cardiovascularevent for the subject comprises a risk score of a cardiovascular eventfor the subject.

Embodiment 192: The computer-implemented method of any one ofEmbodiments 184-191, wherein the prognosis of a cardiovascular event isgenerated by the computer system utilizing an artificial intelligence ormachine learning algorithm.

Embodiment 193: The computer-implemented method of any one ofEmbodiments 184-192, wherein the cardiovascular event comprises one ormore of atherosclerosis, stenosis, or ischemia.

Embodiment 194: The computer-implemented method of any one ofEmbodiments 184-193, wherein the generated treatment plan comprises oneor more of use of statins, lifestyle changes, or surgery.

Embodiment 195: The computer-implemented method of any one ofEmbodiments 184-194, wherein one or more of the coronary arteryidentification algorithm or the plaque identification algorithmcomprises an artificial intelligence or machine learning algorithm.

Embodiment 196: The computer-implemented method of any one ofEmbodiments 184-195, wherein the plaque identification algorithm isconfigured to determine the one or more regions of plaque by determininga vessel wall and lumen wall of the one or more coronary arteries anddetermining a volume between the vessel wall and lumen wall as the oneor more regions of plaque.

Embodiment 197: The computer-implemented method of any one ofEmbodiments 184-196, wherein the medical image comprises a ComputedTomography (CT) image.

Embodiment 198: The computer-implemented method of Embodiment 197,wherein the medical image comprises a non-contrast CT image.

Embodiment 199: The computer-implemented method of Embodiment 197,wherein the medical image comprises a contrast CT image.

Embodiment 200: The computer-implemented method of any one ofEmbodiments 184-196, wherein the medical image comprises a MagneticResonance (MR) image.

Embodiment 201: The computer-implemented method of any one ofEmbodiments 184-196, wherein the medical image is obtained using animaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 202: A computer-implemented method of determiningpatient-specific stent parameters and guidance for implantation based onnon-invasive medical image analysis, the method comprising: accessing,by a computer system, a medical image of a coronary region of a patient,wherein the medical image of the coronary region of the patient isobtained non-invasively; identifying, by the computer system utilizing acoronary artery identification algorithm, one or more coronary arterieswithin the medical image of the coronary region of the patient, whereinthe coronary artery identification algorithm is configured to utilizeraw medical images as input; identifying, by the computer systemutilizing a plaque identification algorithm, one or more regions ofplaque within the one or more coronary arteries identified from themedical image of the coronary region of the patient, wherein the plaqueidentification algorithm is configured to utilize raw medical images asinput; determining, by the computer system, a set of quantified plaqueparameters of the one or more identified regions of plaque from themedical image of the coronary region of the patient, wherein the set ofquantified plaque parameters comprises a ratio or function of volume tosurface area, heterogeneity index, location, geometry, and radiodensityof the one or more regions of plaque within the medical image;determining, by the computer system, a set of stenosis vessel parametersof the one or more coronary arteries within the medical image of thecoronary region of the patient, wherein the set of vessel parameterscomprises volume, curvature, vessel wall, lumen wall, and diameter ofthe one or more coronary arteries within the medical image in thepresence of stenosis; determining, by the computer system, a set ofnormal vessel parameters of the one or more coronary arteries within themedical image of the coronary region of the patient, wherein the set ofvessel parameters comprises volume, curvature, vessel wall, lumen wall,and diameter of the one or more coronary arteries within the medicalimage without stenosis, wherein the set of normal vessel parameters aredetermined by graphically removing from the medical image of thecoronary region of the patient the identified one or more regions ofplaque; determining, by the computer system, a predicted effectivenessof stent implantation for the patient based at least in part on the setof quantified plaque parameters and the set of vessel parameters;generating, by the computer system, patient-specific stent parametersfor the patient when the predicted effectiveness of stent implantationfor the patient is above a predetermined threshold, wherein thepatient-specific stent parameters are generated based at least in parton the set of quantified plaque parameters, the set of vesselparameters, and the set of normal vessel parameters; and generating, bythe computer system, guidance for implantation of a patient-specificstent comprising the patient-specific stent parameters, wherein theguidance for implantation of the patient-specific stent is generatedbased at least in part on the set of quantified plaque parameters andthe set of vessel parameters, wherein the generated guidance forimplantation of the patient-specific stent comprises insertion ofguidance wires and positioning of the patient-specific stent, whereinthe computer system comprises a computer processor and an electronicstorage medium.

Embodiment 203: The computer-implemented method of Embodiment 202,further comprising accessing, by the computer system, apost-implantation medical image of the coronary region of the patientand performing post-implantation analysis.

Embodiment 204: The computer-implemented method of Embodiment 203,further comprising generating, by the computer system, a treatment planfor the patient based at least in part on the post-implantationanalysis.

Embodiment 205: The computer-implemented method of Embodiment 204,wherein the generated treatment plan comprises one or more of use ofstatins, lifestyle changes, or surgery.

Embodiment 206: The computer-implemented method of any one ofEmbodiments 202-205, wherein the set of stenosis vessel parameterscomprises a location, curvature, and diameter of bifurcation of the oneor more coronary arteries.

Embodiment 207: The computer-implemented method of any one ofEmbodiments 202-206, wherein the patient-specific stent parameterscomprise a diameter of the patient-specific stent.

Embodiment 208: The computer-implemented method of Embodiment 207,wherein the diameter of the patient-specific stent is substantiallyequal to the diameter of the one or more coronary arteries withoutstenosis.

Embodiment 209: The computer-implemented method of Embodiment 207,wherein the diameter of the patient-specific stent is less than thediameter of the one or more coronary arteries without stenosis.

Embodiment 210: The computer-implemented method of any one ofEmbodiments 202-209, wherein the predicted effectiveness of stentimplantation for the patient is determined by the computer systemutilizing an artificial intelligence or machine learning algorithm.

Embodiment 211: The computer-implemented method of any one ofEmbodiments 202-210, wherein the patient-specific stent parameters forthe patient are generated by the computer system utilizing an artificialintelligence or machine learning algorithm.

Embodiment 212: The computer-implemented method of any one ofEmbodiments 202-211, wherein one or more of the coronary arteryidentification algorithm or the plaque identification algorithmcomprises an artificial intelligence or machine learning algorithm.

Embodiment 213: The computer-implemented method of any one ofEmbodiments 202-212, wherein the plaque identification algorithm isconfigured to determine the one or more regions of plaque by determininga vessel wall and lumen wall of the one or more coronary arteries anddetermining a volume between the vessel wall and lumen wall as the oneor more regions of plaque.

Embodiment 214: The computer-implemented method of any one ofEmbodiments 202-213, wherein the medical image comprises a ComputedTomography (CT) image.

Embodiment 215: The computer-implemented method of Embodiment 214,wherein the medical image comprises a non-contrast CT image.

Embodiment 216: The computer-implemented method of Embodiment 214,wherein the medical image comprises a contrast CT image.

Embodiment 217: The computer-implemented method of any one ofEmbodiments 202-213, wherein the medical image comprises a MagneticResonance (MR) image.

Embodiment 218: The computer-implemented method of any one ofEmbodiments 202-213, wherein the medical image is obtained using animaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 219: A computer-implemented method of generating apatient-specific report on coronary artery disease for a patient basedon non-invasive medical image analysis, the method comprising:accessing, by a computer system, a medical image of a coronary region ofa patient, wherein the medical image of the coronary region of thepatient is obtained non-invasively; identifying, by the computer systemutilizing a coronary artery identification algorithm, one or morecoronary arteries within the medical image of the coronary region of thepatient, wherein the coronary artery identification algorithm isconfigured to utilize raw medical images as input; identifying, by thecomputer system utilizing a plaque identification algorithm, one or moreregions of plaque within the one or more coronary arteries identifiedfrom the medical image of the coronary region of the patient, whereinthe plaque identification algorithm is configured to utilize raw medicalimages as input; determining, by the computer system, one or morevascular morphology parameters and a set of quantified plaque parametersof the one or more identified regions of plaque from the medical imageof the coronary region of the patient, wherein the set of quantifiedplaque parameters comprises a ratio or function of volume to surfacearea, volume, heterogeneity index, location, geometry, and radiodensityof the one or more regions of plaque within the medical image;quantifying, by the computer system, stenosis and atherosclerosis of thepatient based at least in part on the set of quantified plaqueparameters determined from the medical image; generating, by thecomputer system, one or more annotated medical images based at least inpart on the medical image, the quantified stenosis and atherosclerosisof the patient, and the set of quantified plaque parameters determinedfrom the medical image; determining, by the computer system, a risk ofcoronary artery disease for the patient based at least in part bycomparing the quantified stenosis and atherosclerosis of the patient andthe set of quantified plaque parameters determined from the medicalimage to a known dataset of one or more quantified stenosis andatherosclerosis and one or more quantified plaque parameters derivedfrom one or more medial images of healthy subjects within an age groupof the patient; dynamically generating, by the computer system, apatient-specific report on coronary artery disease for the patient,wherein the generated patient-specific report comprises the one or moreannotated medical images, one or more of the set of quantified plaqueparameters, and determined risk of coronary artery disease, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

Embodiment 220: The computer-implemented method of Embodiment 219,wherein the patient-specific report comprises a cinematic report.

Embodiment 221: The computer-implemented method of Embodiment 220,wherein the patient-specific report comprises content configured toprovide an Augmented Reality (AR) or Virtual Reality (VR) experience.

Embodiment 222: The computer-implemented method of any one ofEmbodiments 219-221, wherein the patient-specific report comprises audiodynamically generated for the patient based at least in part on thequantified stenosis and atherosclerosis of the patient, the set ofquantified plaque parameters determined from the medical image, anddetermined risk of coronary artery disease.

Embodiment 223: The computer-implemented method of any one ofEmbodiments 219-222, wherein the patient-specific report comprisesphrases dynamically generated for the patient based at least in part onthe quantified stenosis and atherosclerosis of the patient, the set ofquantified plaque parameters determined from the medical image, anddetermined risk of coronary artery disease.

Embodiment 224: The computer-implemented method of any one ofEmbodiments 219-223, further comprising generating, by the computersystem, a treatment plan for the patient based at least in part on thequantified stenosis and atherosclerosis of the patient, the set ofquantified plaque parameters determined from the medical image, anddetermined risk of coronary artery disease, wherein the patient-specificreport comprises the generated treatment plan.

Embodiment 225: The computer-implemented method of Embodiment 224,wherein the generated treatment plan comprises one or more of use ofstatins, lifestyle changes, or surgery.

Embodiment 226: The computer-implemented method of any one ofEmbodiments 219-225, further comprising tracking, by the computersystem, progression of coronary artery disease for the patient based atleast in part on comparing one or more of the set of quantified plaqueparameters determined from the medical image against one or moreprevious quantified plaque parameters derived from a previous medicalimage of the patient, wherein the patient-specific report comprises thetracked progression of coronary artery disease.

Embodiment 227: The computer-implemented method of any one ofEmbodiments 219-226, wherein one or more of the coronary arteryidentification algorithm or the plaque identification algorithmcomprises an artificial intelligence or machine learning algorithm.

Embodiment 228: The computer-implemented method of any one ofEmbodiments 219-227, wherein the plaque identification algorithm isconfigured to determine the one or more regions of plaque by determininga vessel wall and lumen wall of the one or more coronary arteries anddetermining a volume between the vessel wall and lumen wall as the oneor more regions of plaque.

Embodiment 229: The computer-implemented method of any one ofEmbodiments 219-228, wherein the medical image comprises a ComputedTomography (CT) image.

Embodiment 230: The computer-implemented method of Embodiment 229,wherein the medical image comprises a non-contrast CT image.

Embodiment 231: The computer-implemented method of Embodiment 229,wherein the medical image comprises a contrast CT image.

Embodiment 232: The computer-implemented method of any one ofEmbodiments 219-228, wherein the medical image comprises a MagneticResonance (MR) image.

Embodiment 233: The computer-implemented method of any one ofEmbodiments 219-228, wherein the medical image is obtained using animaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 234: A system comprising: at least one non-transitorycomputer storage medium configured to at least store computer-executableinstructions, a set of computed tomography (CT) images of a patient’scoronary vessels, vessel labels, and artery information associated withthe set of CT images including information of stenosis, plaque, andlocations of segments of the coronary vessels; one or more computerhardware processors in communication with the at least onenon-transitory computer storage medium, the one or more computerhardware processors configured to execute the computer-executableinstructions to at least: generate and display a user interface a firstpanel including an artery tree comprising a three-dimensional (3D)representation of coronary vessels depicting coronary vessels identifiedin the CT images, and including segment labels related to the arterytree, the artery tree not including heart tissue between branches of theartery tree; in response to an input on the user interface indicatingthe selection of a coronary vessel in the artery tree in the firstpanel, generate and display on the user interface a second panelillustrating at least a portion of the selected coronary vessel in atleast one straightened multiplanar vessel (SMPR) view; generate anddisplay on the user interface a third panel showing a cross-sectionalview of the selected coronary vessel, the cross-sectional view generatedusing one of the set of CT images of the selected coronary vessel,wherein locations along the at least one SMPR view are each associatedwith one of the CT images in the set of CT images such that a selectionof a particular location along the coronary vessel in the at least oneSMPR view displays the associated CT image in the cross-sectional viewin the third panel; and in response to an input on the third panelindicating a first location along the selected coronary artery in the atleast one SMPR view, display a cross-sectional view associated with theselected coronary artery at the first location in the third panel.

Embodiment 235: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to, in response to an input on thesecond panel of the user interface indicating a second location alongthe selected coronary artery in the at least one SMPR view, display theassociated CT scan associated with the second location in across-sectional view in the third panel.

Embodiment 236: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to: in response to a second input onthe user interface indicating the selection of a second coronary vesselin the artery tree displayed in the first panel, generate and display inthe second panel at least a portion of the selected second coronaryvessel in at least one straightened multiplanar vessel (SMPR) view, andgenerate and display on the third panel a cross-sectional view of theselected second coronary vessel, the cross-sectional view generatedusing one of the set of CT images of the selected second coronaryvessel, wherein locations along the selected second coronary artery inthe at least one SMPR view are each associated with one of the CT imagesin the set of CT images such that a selection of a particular locationalong the second coronary vessel in the at least one SMPR view displaysthe associated CT image in the cross-sectional view in the third panel.

Embodiment 237: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to identify thevessel segments using a machine learning algorithm that processes the CTimages prior to storing the artery information on the at least onenon-transitory computer storage medium.

Embodiment 238: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to generate and display on the userinterface in a fourth panel a cartoon artery tree, the cartoon arterytree comprising a non-patient specific graphical representation of acoronary artery tree, and wherein in response to a selection of a vesselsegment in the cartoon artery tree, a view of the selected vesselsegment is displayed in a panel of the user interface in a SMPR view,and upon selection of a location of the vessel segment displayed in theSMPR view, generate and display in the user interface a panel thatdisplays information about the selected vessel at the selected location.

Embodiment 239: The system of embodiment 238, wherein the displayedinformation includes information relating to stenosis and plaque of theselected vessel.

Embodiment 240: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to generate and segment name labels,proximal to a respective segment on the artery tree, indicative of thename of the segment.

Embodiment 241: The system of embodiment 240, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to, in response to an input selectionof a first segment name label displayed on the user interface, generateand display on the user interface a panel having a list of vesselsegment names and indicating the current name of the selected vesselsegment; and in response to an input selection of a second segment namelabel on the list, replace the first segment name label with the secondsegment name label of the displayed artery tree in the user interface.

Embodiment 242: The system of embodiment 234, wherein the at least oneSMPR view of the selected coronary vessel comprises at least two SMPRviews of the selected coronary vessel displayed adjacently at arotational interval.

Embodiment 243: The system of embodiment 234, wherein the at least oneSMPR view include four SMPR views displayed at a relative rotation of0°, 22.5°, 45°, and 67.5°.

Embodiment 244: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to, in response to a user input, rotatethe at least one SMPR view in increments of 1°.

Embodiment 245: The system of embodiment 234, wherein the artery tree,the at least one SMPR view, and the cross-sectional view are displayedconcurrently on the user interface.

Embodiment 246: The system of embodiment 245, wherein the artery tree isdisplayed in a center portion of the user panel, the cross-sectionalview is displayed in a center portion of the user interface above orbelow the artery tree, and the at least one SMPR view are displayed onone side of the center portion of the user interface.

Embodiment 247: The system of embodiment 246, wherein the one or morecomputer hardware processors are further configured to generate anddisplay, on one side of the center portion of the user interface, one ormore anatomical plane views corresponding to the selected coronaryartery, the anatomical plane views of the selected coronary vessel basedon the CT images.

Embodiment 248: The system of embodiment 247, wherein the anatomicalplane views comprise three anatomical plane views.

Embodiment 249: The system of embodiment 247, wherein the anatomicalplane views comprise at least one of an axial plane view, a coronalplane view, or a sagittal plane view.

Embodiment 250: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to receive arotation input on the user interface, and rotate the at least one SMPRviews incrementally based on the rotation input.

Embodiment 251: The system of embodiment 234, wherein the at least onenon-transitory computer storage medium is further configured to at leaststore vessel wall information including information indicative of thelumen and the vessel walls of the coronary artery vessels, and whereinthe one or more computer hardware processors are further configured tographically display lumen and vessel wall information corresponding tothe coronary vessel displayed in the cross-sectional view in the thirdpanel.

Embodiment 252: The system of embodiment 251, wherein and one or morecomputer hardware processors are further configured to displayinformation of the lumen and the vessel wall on the user interface basedon the selected portion of the coronary vessel in the at least one SMPRview.

Embodiment 253: The system of embodiment 251, wherein and one or morecomputer hardware processors are further configured to displayinformation of plaque based on the selected portion of the coronaryvessel in the at least one SMPR view.

Embodiment 254: The system of embodiment 251, wherein and one or morecomputer hardware processors are further configured to displayinformation of stenosis based on the selected portion of the coronaryvessel in the at least one SMPR view.

Embodiment 255: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to generate and display on the userinterface a cartoon artery tree, the cartoon artery tree being anon-patient specific graphical representation of an artery tree, whereinportions of the artery tree are displayed in a color that corresponds toa risk level.

Embodiment 256: The system of embodiment 255, wherein the risk level isbased on stenosis.

Embodiment 257: The system of embodiment 255, wherein the risk level isbased on a plaque.

Embodiment 258: The system of embodiment 255, wherein the risk level isbased on ischemia.

Embodiment 259: The system of embodiment 255, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to, in response to selecting a portionof the cartoon artery tree, displaying on the second panel a SMPR viewof the vessel corresponding to the selected portion of the cartoonartery tree, and displaying on the third panel a cross-sectional view ofcorresponding to the selected portion of the cartoon artery tree.

Embodiment 260: A system comprising: means for storingcomputer-executable instructions, a set of computed tomography (CT)images of a patient’s coronary vessels, vessel labels, and arteryinformation associated with the set of CT images including informationof stenosis, plaque, and locations of segments of the coronary vessels;and means for executing the computer-executable instructions to atleast: generate and display a user interface a first panel including anartery tree comprising a three-dimensional (3D) representation ofcoronary vessels based on the CT images and depicting coronary vesselsidentified in the CT images, and depicting segment labels, the arterytree not including heart tissue between branches of the artery tree; inresponse to an input on the user interface indicating the selection of acoronary vessel in the artery tree in the first panel, generate anddisplay on the user interface a second panel illustrating at least aportion of the selected coronary vessel in at least one straightenedmultiplanar vessel (SMPR) view; generate and display on the userinterface a third panel showing a cross-sectional view of the selectedcoronary vessel, the cross-sectional view generated using one of the setof CT images of the selected coronary vessel, wherein locations alongthe at least one SMPR view are each associated with one of the CT imagesin the set of CT images such that a selection of a particular locationalong the coronary vessel in the at least one SMPR view displays theassociated CT image in the cross-sectional view in the third panel; andin response to an input on the user interface indicating a firstlocation along the selected coronary artery in the at least one SMPRview, display the associated CT scan associated with the in thecross-sectional view in the third panel.

Embodiment 261: A method for analyzing CT images and correspondinginformation, the method comprising: storing computer-executableinstructions, a set of computed tomography (CT) images of a patient’scoronary vessels, vessel labels, and artery information associated withthe set of CT images including information of stenosis, plaque, andlocations of segments of the coronary vessels; generating and displayingin a user interface a first panel including an artery tree comprising athree-dimensional (3D) representation of coronary vessels based on theCT images and depicting coronary vessels identified in the CT images,and depicting segment labels, the artery tree not including heart tissuebetween branches of the artery tree; receiving a first input indicatinga selection of a coronary vessel in the artery tree in the first panel;in response to the first input, generating and displaying on the userinterface a second panel illustrating at least a portion of the selectedcoronary vessel in at least one straightened multiplanar vessel (SMPR)view; generating and displaying on the user interface a third panelshowing a cross-sectional view of the selected coronary vessel, thecross-sectional view generated using one of the set of CT images of theselected coronary vessel, wherein locations along the at least one SMPRview are each associated with one of the CT images in the set of CTimages such that a selection of a particular location along the coronaryvessel in the at least one SMPR view displays the associated CT image inthe cross-sectional view in the third panel; receiving a second input onthe user interface indicating a first location along the selectedcoronary artery in the at least one SMPR view; and in response to thesecond input, displaying the associated CT scan associated in thecross-sectional view in the third panel, wherein the method is performedby one or more computer hardware processors executingcomputer-executable instructions in communication stored on one or morenon-transitory computer storage mediums.

Embodiment 262: The method of embodiment 261, further comprising, inresponse to an input on the second panel of the user interfaceindicating a second location along the selected coronary artery in theat least one SMPR view, display the associated CT scan associated withthe second location in a cross-sectional view in the third panel.

Embodiment 263: The method of any one of embodiments 261 and 262,further comprising: in response to a second input on the user interfaceindicating the selection of a second coronary vessel in the artery treedisplayed in the first panel, generating and displaying in the secondpanel at least a portion of the selected second coronary vessel in atleast one straightened multiplanar vessel (SMPR) view, and generatingand displaying on the third panel a cross-sectional view of the selectedsecond coronary vessel, the cross-sectional view generated using one ofthe set of CT images of the selected second coronary vessel, whereinlocations along the selected second coronary artery in the at least oneSMPR view are each associated with one of the CT images in the set of CTimages such that a selection of a particular location along the secondcoronary vessel in the at least one SMPR view displays the associated CTimage in the cross-sectional view in the third panel.

Embodiment 264: The method of any one of embodiments 261-263, furthercomprising generating and displaying on the user interface in a fourthpanel a cartoon artery tree, the cartoon artery tree comprising anon-patient specific graphical representation of a coronary artery tree,and wherein in response to a selection of a vessel segment in thecartoon artery tree, a view of the selected vessel segment is displayedin a panel of the user interface in a SMPR view, and upon selection of alocation of the vessel segment displayed in the SMPR view, generatingand displaying in the user interface a panel that displays informationabout the selected vessel at the selected location.

Embodiment 265: The method of embodiment 264, wherein the displayedinformation includes information relating to stenosis and plaque of theselected vessel.

Embodiment 266: The method of any one of embodiments 261-265, furthercomprising generating and displaying segment name labels, proximal to arespective segment on the artery tree, indicative of the name of thesegment, using the stored artery information.

Embodiment 267: The method of any one of embodiments 261-266, furthercomprising, in response to an input selection of a first segment namelabel displayed on the user interface, generating and displaying on theuser interface a panel having a list of vessel segment names andindicating the current name of the selected vessel segment, and inresponse to an input selection of a second segment name label on thelist, replacing the first segment name label with the second segmentname label of the displayed artery tree in the user interface.

Embodiment 268: The method of any one of embodiments 261-267, furthercomprising generating and displaying a tool bar on a fourth panel of theuser interface, the tool bar comprising tools to add, delete, or reviseartery information displayed on the user interface.

Embodiment 269: The method of embodiment 268, wherein the tools on thetoolbar include a lumen wall tool, a snap to vessel wall tool, a snap tolumen wall tool, vessel wall tool, a segment tool, a stenosis tool, aplaque overlay tool a snap to centerline tool, chronic total occlusiontool, stent tool, an exclude tool, a tracker tool, or a distancemeasurement tool.

Embodiment 270: The method of embodiment 268, wherein the tools on thetoolbar include a lumen wall tool, a snap to vessel wall tool, a snap tolumen wall tool, vessel wall tool, a segment tool, a stenosis tool, aplaque overlay tool a snap to centerline tool, chronic total occlusiontool, stent tool, an exclude tool, a tracker tool, and a distancemeasurement tool.

Embodiment 271: A normalization device configured to facilitatenormalization of medical images of a coronary region of a subject for analgorithm-based medical imaging analysis, the normalization devicecomprising: a substrate having a width, a length, and a depth dimension,the substrate having a proximal surface and a distal surface, theproximal surface adapted to be placed adjacent to a surface of a bodyportion of a patient; a plurality of compartments positioned within thesubstrate, each of the plurality of compartments configured to hold asample of a known material, wherein: a first subset of the plurality ofcompartments hold samples of a contrast material with differentconcentrations, a second subset of the plurality of compartments holdsamples of materials representative of materials to be analyzed by thealgorithm-based medical imaging analysis, and a third subset of theplurality of compartments hold samples of phantom materials.

Embodiment 272: The normalization device of Embodiment 271, wherein thecontrast material comprises one of iodine, Gad, Tantalum, Tungsten,Gold, Bismuth, or Ytterbium.

Embodiment 273: The normalization device of any of Embodiments 271-272,wherein the samples of materials representative of materials to beanalyzed by the algorithm-based medical imaging analysis comprise atleast two of calcium 1000HU, calcium 220HU, calcium 150HU, calcium130HU, and a low attenuation (e.g., 30 HU) material.

Embodiment 274: The normalization device of any of Embodiments 271-273,wherein the samples of phantom materials comprise one or more of water,fat, calcium, uric acid, air, iron, or blood.

Embodiment 275: The normalization device of any of Embodiments 271-274,further comprising one or more fiducials positioned on or in thesubstrate for determining the alignment of the normalization device inan image of the normalization device such that the position in the imageof each of the one or more compartments in the first arrangement can bedetermined using the one or more fiducials.

Embodiment 276: The normalization device of any of Embodiments 271-275,wherein the substrate comprises a first layer, and at least some of theplurality of compartments are positioned in the first layer in a firstarrangement.

Embodiment 277: The normalization device of Embodiment 276, wherein thesubstrate further comprises a second layer positioned above the firstlayer, and at least some of the plurality of compartments are positionedin the second layer including in a second arrangement.

Embodiment 278: The normalization device of Embodiment 277, furthercomprising one or more additional layers positioned above the secondlayer, and at least some of the plurality of compartments are positionedwithin the one or more additional layers.

Embodiment 279: The normalization device of any one of Embodiments271-278, wherein at least one of the compartments is configured to beself-sealing such that the material can be injected into theself-sealing compartment and the compartment seals to contain theinjected material.

Embodiment 280: The normalization device of any of Embodiments 271-279,further comprising an adhesive on the proximal surface of the substrateand configured to adhere the normalization device to the body portionpatient.

Embodiment 281: The normalization device of any of Embodiments 271-280,further comprising a heat transfer material designed to transfer heatfrom the body portion of the patient to the material in the one or morecompartments.

Embodiment 282: The normalization device of any of Embodiments 271-280,further comprising an adhesive strip having a proximal side and a distalside, the proximal side configured to adhere to the body portion, theadhesive strip including a fastener configured to removably attach tothe proximal surface of the substrate.

Embodiment 283: The normalization device of Embodiment 282, wherein thefastener comprises a first part of a hook-and-loop fastener, and thefirst layer comprises a corresponding second part of the hook-and-loopfastener.

Embodiment 284: The normalization device of any of Embodiments 271-283,wherein substrate a flexible material to allow the substrate to conformto the shape of the body portion.

Embodiment 285: The normalization device of any of Embodiments 271-284,wherein the first arrangement includes a circular-shaped arrangements ofthe compartments.

Embodiment 286: The normalization device of any of Embodiments 271-284,wherein the first arrangement includes a rectangular-shaped arrangementsof the compartments.

Embodiment 287: The normalization device of any of Embodiments 271-286,wherein the material in at least two compartments is the same.

Embodiment 288: The normalization device of any of Embodiments 271-287,wherein at least one of a length, a width or a depth dimension of acompartment is less than 0.5 mm.

Embodiment 289: The normalization device of any of Embodiments 271-287,wherein a width dimension of the compartments is between 0.1 mm and 1mm.

Embodiment 290: The normalization device of Embodiment 289, wherein alength dimension of the compartments is between 0.1 mm and 1 mm.

Embodiment 291: The normalization device of Embodiment 290, wherein adepth dimension of the compartments is between 0.1 mm and 1 mm.

Embodiment 292: The normalization device of any of Embodiments 271-287,wherein at least one of the length, width or depth dimension of acompartment is greater than 1.0 mm.

Embodiment 293: The normalization device of any of Embodiments 271-287,wherein dimensions of some or all of the compartments in thenormalization device are different from each other allowing a singlenormalization device to have a plurality of compartments havingdifferent dimensions such that the normalization device can be used invarious medical image scanning devices having different resolutioncapabilities.

Embodiment 294: The normalization device of any of Embodiments 271-287,wherein the normalization device includes a plurality of compartmentswith differing dimensions such that the normalization device can be usedto determine the actual resolution capability of the scanning device.

Embodiment 295: A normalization device, comprising: a first layer havinga width, length, and depth dimension, the first layer having a proximalsurface and a distal surface, the proximal surface adapted to be placedadjacent to a surface of a body portion of a patient, the first layerincluding one or more compartments positioned in the first layer in afirst arrangement, each of the one or more compartments containing aknown material; and one or more fiducials for determining the alignmentof the normalization device in an image of the normalization device suchthat the position in the image of each of the one or more compartmentsin the first arrangement be the determined using the one or morefiducials.

Embodiment 296: The normalization device of Embodiment 295, furthercomprising a second layer having a width, length, and depth dimension,the second layer having a proximal surface and a distal surface, theproximal surface adjacent to the distal surface of the first layer, thesecond layer including one or more compartments positioned in the secondlayer in a second arrangement, each of the one or more compartments ofthe second layer containing a known material.

Embodiment 297: The normalization device of Embodiment 296, furthercomprising one or more additional layers each having a width, length,and depth dimension, the one or more additional layers having a proximalsurface and a distal surface, the proximal surface facing the secondlayer and each of the one or more layers positioned such that the secondlayer is between the first layer and the one or more additional layers,each of the one or more additional layers respectively including one ormore compartments positioned in each respective one or more additionallayers layer in a second arrangement, each of the one or morecompartments of the one or more additional layers containing a knownmaterial.

Embodiment 298: The normalization device of any one of Embodiments295-297, wherein at least one of the compartments is configured to beself-sealing such that the material can be injected into theself-sealing compartment and the compartment seals to contain theinjected material.

Embodiment 299: The normalization device of Embodiment 295, furthercomprising an adhesive on the proximal surface of the first layer.

Embodiment 300: The normalization device of Embodiment 295, furthercomprising a heat transfer material designed to transfer heat from thebody portion of the patient to the material in the one or morecompartments.

Embodiment 301: The normalization device of Embodiment 295, furthercomprising an adhesive strip having a proximal side and a distal side,the proximal side configured to adhere to the body portion, the adhesivestrip including a fastener configured to removably attach to theproximal surface of the first layer.

Embodiment 302: The normalization device of Embodiment 301, wherein thefastener comprises a first part of a hook-and-loop fastener, and thefirst layer comprises a corresponding second part of the hook-and-loopfastener.

Embodiment 303: The normalization device of Embodiment 295, wherein thenormalization device comprises a flexible material to allow thenormalization device to conform to the shape of the body portion.

Embodiment 304: The normalization device of Embodiment 295, wherein thefirst arrangement includes a circular-shaped arrangements of thecompartments.

Embodiment 305: The normalization device of Embodiment 295, wherein thefirst arrangement includes a rectangular-shaped arrangements of thecompartments.

Embodiment 306: The normalization device of Embodiment 295, wherein thematerial in at least two compartments of the first layer is the same.

Embodiment 307: The normalization device of any of Embodiment 296 or297, wherein the material in at least two compartments of any of thelayers is the same.

Embodiment 308: The normalization device of Embodiment 295, wherein atleast one of the one or more compartments include a contrast material.

Embodiment 309: The normalization device of Embodiment 308, wherein thecontrast material comprises one of iodine, Gad, Tantalum, Tungsten,Gold, Bismuth, or Ytterbium.

Embodiment 310: The normalization device of Embodiment 295, wherein atleast one of the one or more compartments include a materialrepresentative of a studied variable.

Embodiment 311: The normalization device of Embodiment 309, wherein thestudied variable is representative of calcium 1000HU, calcium 220HU,calcium 150HU, calcium 130HU, or a low attenuation (e.g., 30 HU)material.

Embodiment 312: The normalization device of Embodiment 295, wherein atleast one of the one or more compartments include a phantom.

Embodiment 313: The normalization device of Embodiment 312, wherein thephantom comprises one of water, fat, calcium, uric acid, air, iron, orblood.

Embodiment 314: The normalization device of Embodiment 295, wherein thefirst arrangement includes at least one compartment that contains acontrast agent, at least one compartment that includes a studiedvariable and at least one compartment that includes a phantom.

Embodiment 315: The normalization device of Embodiment 295, wherein thefirst arrangement includes at least one compartment that contains acontrast agent and at least one compartment that includes a studiedvariable.

Embodiment 316: The normalization device of Embodiment 295, wherein thefirst arrangement includes at least one compartment that contains acontrast agent and at least one compartment that includes a phantom.

Embodiment 317: The normalization device of Embodiment 295, wherein thefirst arrangement includes at least one compartment that contains astudied variable and at least one compartment that includes a phantom.

Embodiment 318: The normalization device of Embodiment 271, wherein thefirst arrangement of the first layer includes at least one compartmentthat contains a contrast agent, at least one compartment that includes astudied variable and at least one compartment that includes a phantom,and the second arrangement of the second layer includes at least onecompartment that contains a contrast agent, at least one compartmentthat includes a studied variable and at least one compartment thatincludes a phantom.

Embodiment 319: The normalization device of Embodiment 295, wherein atleast one of the length, width or depth dimension of a compartment isless than 0.5 mm.

Embodiment 320: The normalization device of Embodiment 295, wherein thewidth dimension of the compartments is between 0.1 mm and 1 mm.

Embodiment 321: The normalization device of Embodiment 295, wherein thelength dimension of the compartments is between 0.1 mm and 1 mm.

Embodiment 322: The normalization device of Embodiment 295, wherein thedepth (or height) dimension of the compartments is between 0.1 mm and 1mm.

Embodiment 323: The normalization device of Embodiment 295, wherein atleast one of the length, width or depth dimension of a compartment isgreater than 1.0 mm.

Embodiment 324: The normalization device of any one of Embodiments295-297, wherein the dimensions of some or all of the compartments inthe normalization device are different from each other allowing a singlenormalization device to have a plurality of compartments havingdifferent dimension such that the normalization device can be used invarious medical image scanning devices having different resolutioncapabilities.

Embodiment 325: The normalization device of any one of Embodiments295-297, wherein the normalization device includes a plurality ofcompartments with differing dimensions such that the normalizationdevice can be used to determine the actual resolution capability of thescanning device.

Embodiment 326: A computer-implemented method for normalizing medicalimages for an algorithm-based medical imaging analysis, whereinnormalization of the medical images improves accuracy of thealgorithm-based medical imaging analysis, the method comprising:accessing, by a computer system, a first medical image of a region of asubject and the normalization device, wherein the first medical image isobtained non-invasively, and wherein the normalization device comprisesa substrate comprising a plurality of compartments, each of theplurality of compartments holding a sample of a known material;accessing, by the computer system, a second medical image of a region ofa subject and the normalization device, wherein the second medical imageis obtained non-invasively, and wherein the first medical image and thesecond medical image comprise at least one of the following: one or morefirst variable acquisition parameters associated with capture of thefirst medical image differ from a corresponding one or more secondvariable acquisition parameters associated with capture of the secondmedical image, a first image capture technology used to capture thefirst medical image differs from a second image capture technology usedto capture the second medical image, and a first contrast agent usedduring the capture of the first medical image differs from a secondcontrast agent used during the capture of the second medical image;identifying, by the computer system, image parameters of thenormalization device within the first medical image; generating anormalized first medical image for the algorithm-based medical imaginganalysis based in part on the first identified image parameters of thenormalization device within the first medical image; identifying, by thecomputer system, image parameters of the normalization device within thesecond medical image; and generating a normalized second medical imagefor the algorithm-based medical imaging analysis based in part on thesecond identified image parameters of the normalization device withinthe second medical image, wherein the computer system comprises acomputer processor and an electronic storage medium.

Embodiment 327: The computer-implemented method of Embodiment 326,wherein the algorithm-based medical imaging analysis comprises anartificial intelligence or machine learning imaging analysis algorithm,and wherein the artificial intelligence or machine learning imaginganalysis algorithm was trained using images that included thenormalization device.

Embodiment 328: The computer-implemented method of any of Embodiments326-327, wherein the first medical image and the second medical imageeach comprise a CT image and the one or more first variable acquisitionparameters and the one or more second variable acquisition parameterscomprise one or more of a kilovoltage (kV), kilovoltage peak (kVp), amilliamperage (mA), or a method of gating.

Embodiment 329: The computer-implemented method of Embodiment 328,wherein the method of gating comprises one of prospective axialtriggering, retrospective ECG helical gating, and fast pitch helical.

Embodiment 330: The computer-implemented method of any of Embodiments326-329, wherein the first image capture technology and the second imagecapture technology each comprise one of a dual source scanner, a singlesource scanner, Dual source vs. single source scanners dual energy,monochromatic energy, spectral CT, photon counting, and differentdetector materials.

Embodiment 331: The computer-implemented method of any of Embodiments326-330, wherein the first contrast agent and the second contrast agenteach comprise one of an iodine contrast of varying concentration or anon-iodine contrast agent.

Embodiment 332: The computer-implemented method of any of Embodiments326-327, wherein the first image capture technology and the second imagecapture technology each comprise one of CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 333: The computer-implemented method of any of Embodiments326-332, wherein a first medical imager that captures the first medicalimager is different than a second medical image that capture the secondmedical image.

Embodiment 334: The computer-implemented method of any of Embodiments326-333, wherein the subject of the first medical image is differentthan the subject of the first medical image.

Embodiment 335: The computer-implemented method of any of Embodiments326-333, wherein the subject of the first medical image is the same asthe subject of the second medical image.

Embodiment 336: The computer-implemented method of any of Embodiments326-333, wherein the subject of the first medical image is differentthan the subject of the second medical image.

Embodiment 337: The computer-implemented method of any of Embodiments326-336, wherein the capture of the first medical image is separatedfrom the capture of the second medical image by at least one day.

Embodiment 338: The computer-implemented method of any of Embodiments326-337, wherein the capture of the first medical image is separatedfrom the capture of the second medical image by at least one day.

Embodiment 339: The computer-implemented method of any of Embodiments326-338, wherein a location of the capture of the first medical image isgeographically separated from a location of the capture of the secondmedical image.

Embodiment 340: The computer-implemented method of any of Embodiments326-339, wherein the normalization device comprises the normalizationdevice of any of Embodiments 271-325.

Embodiment 341: The computer-implemented method of any of Embodiments326-340, wherein the region of the subject comprises a coronary regionof the subject.

Embodiment 342: The computer-implemented method of any of Embodiments326-341, wherein the region of the subject comprises one or morecoronary arteries of the subject.

Embodiment 343: The computer-implemented method of any of Embodiments326-340, wherein the region of the subject comprises one or more ofcarotid arteries, renal arteries, abdominal aorta, cerebral arteries,lower extremities, or upper extremities of the subject.

Additional Detail - Normalization Device

As described above and throughout this application, in some embodiments,a normalization device may be used to normalize and/or calibrate amedical image of a patient before that image is analyzed by analgorithm-based medical imaging analysis. This section providesadditional detail regarding embodiments of the normalization device andembodiments of the use thereof.

In general, the normalization device can be configured to provide atleast two functions: (1) the normalization device can be used tonormalize and calibrate a medical image to a known relative spectrum;and (2) the normalization device can be used to calibrate a medicalimage such that pixels within the medical image representative ofvarious materials can be normalized and calibrated to materials of knownabsolute density-this can facilitate and allow identification ofmaterials within the medical image. In some embodiments, each of thesetwo functions play a role in providing accurate algorithm-based medicalimaging analysis as will be described below.

For example, it can be important to normalize and calibrate a medicalimage to a known relative spectrum. As a specific example, a CT scangenerally produces a medical image comprising pixels represented in grayscale. However, when two CT scans are taken under different conditions,the gray scale spectrum in the first image may not (and likely will not)match the gray scale spectrum of the second image. That is, even if thefirst and second CT images represent the same subject, the specificgrayscale values in the two images, even for the same structure may not(and likely will not) match. A pixel or group of pixels within the firstimage that represents a calcified plaque buildup within a blood vessel,may (and likely will) appear different (a different shade of gray, forexample, darker or lighter) than a pixel or group of pixels within thesecond image, even if the pixel or groups of pixels within the first andsecond images is representative of the same calcified plaque buildup.

Moreover, the differences between the first and second images may not belinear. That is, the second image may not be uniformly lighter or darkerthan the first image, such that it is not possible to use a simplelinear transform to cause the two images to correspond. Rather, it ispossible that, for example, some regions in the first image may appearlighter than corresponding regions in the second image, while at thesame time, other regions in the first image may appear darker thancorresponding regions in the second image. In order to normalize the twomedical images such that each appears on the same grayscale spectrum, anon-linear transform may be necessary. Use of the normalization devicecan facilitate and enable such a non-linear transform such thatdifferent medical images, that otherwise would not appear to have thesame grayscale spectrum, are adjusted so that the same grayscalespectrum is used in each image.

A wide variety of factors can contribute to different medical images,even of the same subject, falling on different grayscale spectrums. Thiscan include, for example, different medical imaging machine parameters,different parameters associated with the patient, differences incontrast agents used, and/or different medical image acquisitionparameters.

It can be important to normalize and calibrate a medical image to aknown relative spectrum to facilitate the algorithm-based analysis ofthe medical image. As described herein, some algorithm-based medicalimage analysis can be performed using artificial intelligence and/ormachine learning systems. Such artificial intelligence and/or machinelearning systems can be trained using a large number of medical images.The training and performance of such artificial intelligence and/ormachine learning systems can be improved when the medical images are allnormalized and calibrated to the same or similar relative scale.

Additionally, the normalization device can be used to normalize orcalibrate a medical image such that pixels within the medical imagerepresentative of various materials can be normalized and calibrated tomaterials of known absolute density. For example, when analyzing animage of a coronary region of to characterize, for example, calcifiedplaque buildup, it can be important to accurately determine which pixelsor groups of pixels within the medical image correspond to regions ofcalcified plaque buildup. Similarly, it can be important to be able toaccurately identify contrast agents, blood, vessel walls, fat, and othersamples within the image. The use of normalization device can facilitateand enable identification of specific materials within the medicalimage.

The normalization devices described throughout this application can beconfigured to achieve these two functions. In particular, anormalization device can include a substrate or body configured withcompartments that hold different samples. The arrangement (e.g., thespatial arrangement) of the samples is known, as well as othercharacteristics associated with each of the samples, such as thematerial of sample, the volume of the sample, the absolute density ofthe sample, and the relative density of the sample relative to that ofthe other samples in the normalization device. During use, in someembodiments, the normalization device can be included in the medicalimager with the patient, such that an image of the normalizationdevice—including the known samples positioned therein—appears in theimage. An image-processing algorithm can be configured to recognize thenormalization device within the image and use the known samples of thenormalization device to perform the two functions described above.

For example, the image-processing algorithm can detect the known sampleswithin the medical image and use the known samples to adjust the medicalimage such that it uses a common or desired relative spectrum. Forexample, if the normalization device includes a sample of calcium of agiven density, then that sample of calcium will appear with a certaingrayscale value within the image. Due to the various differentconditions under which the medical image was taken, however, theparticular grayscale value within the image will likely not correspondto the desired relative spectrum. The image-processing algorithm canthen adjust the grayscale value in the image such that it falls at theappropriate location on the desired relative spectrum. At the same time,the image-processing algorithm can adjust other pixels within the imagethat do not correspond to the normalization device but that share thesame grayscale value within the medical image, such that those pixelsfall at the appropriate location on the desired relative spectrum. Thiscan be done for all pixels in the image. As noted previously, thistransformation may not be linear. Once complete, however, the pixels ofthe medical image will be adjusted such that they all fall on thedesired relative grayscale spectrum. In this way, two images of the samesubject captured under different conditions, and thus initiallyappearing differently, can be adjusted so that they appear the same(e.g., appearing on the same relative grayscale spectrum).

Additionally, the normalization device can be used to identifyparticular materials within the medical image. For example, because thesamples of the normalization device are known (e.g., known material,volume, position, absolute density, and/or relative density), pixelsrepresentative of the patient’s anatomy can be compared against thematerials of the normalization device (or a scale established by thematerials of the normalization device) such that the materials of thepatient’s anatomy corresponding to the pixels can be identified. As asimple example, the normalization device can include a sample of calciumof a given density. Pixels that appear the same as the pixels thatcorrespond to the sample of calcium can be identified as representingcalcium having the same density as the sample.

In some embodiments, the normalization device is designed such that thesamples contained therein correspond to the disease or condition forwhich the resulting image will be analyzed, the materials within theregion of interest of the patient’s anatomy, and/or the type of medicalimager that will be used. By using a normalization device within theimage, the image-processing algorithms described throughout thisapplication can be easily expanded for use with other imagingmodalities, including new imaging modalities now under development oryet to be developed. This is because, when these new imaging modalitiescome online, suitable normalization devices can be designed for usetherewith.

Further, although this application primarily describes use of thenormalization device for diagnosis and treatment of coronary conditions,other normalization devices can be configured for use in other types ofmedical procedures or diagnosis. This can be done by selecting samplesthat are most relevant to the procedure to be performed or disease to beanalyzed.

The normalization devices described in this application aredistinguishable from conventional phantom devices that are commonly usedin medical imaging applications. Conventional phantom devices aretypically used to calibrate a medical imager to ensure that it isworking properly. For example, conventional phantom devices are oftenimaged by themselves to ensure that the medical image produces anaccurate representation of the phantom device. Conventional phantomdevices are imaged periodically to verify and calibrate the machineitself. These phantom devices, are not, however, imaged with the patientand/or used to calibrate or normalize an image of the patient.

In contrast, the normalization device is often imaged directly with thepatient, especially where the size of the normalization device and theimaging modality permit the normalization device and the patient to beimaged concurrently. If concurrent image is not possible, or in otherembodiments, the normalization device can be imaged separately from thepatient. However, in these cases, it is important that the image of thepatient and the image of the normalization device be imaged under thesame conditions. Rather than verifying that the imaging device isfunctioning properly, the normalization device is used during animage-processing algorithm to calibrate and normalize the image,providing the two functions discussed above.

To further illustrate the difference between conventional phantomdevices and the normalization device, it will be noted that use of thenormalization device does not replace the use of a conventional phantom.Rather, both may be used during an imaging procedure. For example,first, a conventional phantom can be imaged alone. The resulting imageof the phantom can be reviewed and analyzed to determine whether theimaging device is correctly calibrated. If it is, the normalizationdevice and the patient can be imaged together. The resulting image canbe analyzed to detect the normalization device within the image, adjustthe pixels of the image based on the representation of the normalizationdevice within the image, and then, identify specific materials withinthe image using the normalization device as described above.

Several embodiments of normalization devices have been described abovewith reference to FIGS. 12A-12I. FIG. 15 present another embodiment of anormalization device 1500. In the illustrated embodiment, thenormalization device 1500 is configured for use with medical images of acoronary region of a patient for analysis and diagnosis of coronaryconditions; however, the normalization device 1500 may also be used ormay be modified for use with other types of medical images and for othertypes of medical conditions. As will be described below, in theillustrated embodiment, the normalization device 1500 is configured soas to mimic a blood vessel of a patient, and thus may be particularlysuitable for use with analysis and diagnosis of conditions involving apatient’s blood vessels.

As shown in FIG. 15 , the normalization device 1500 comprises asubstrate having a plurality of compartments holding samples formedtherein. In the illustrated embodiment, the samples are labeled A1-A4,B1-B4, and C1-C4. As shown in FIG. 15 , the samples A1-A4 are positionedtowards the center of the normalization device 1500, while the samplesB1-B4 and C1-C4 are generally arranged around the samples A1-A4. Foreach of the samples, the material, volume, absolute density, relativedensity, and spatial configuration is known.

The samples themselves can be selected such that normalization device1500 generally corresponds to a cross-sectional blood sample. Forexample, in one embodiment, the samples A1-A4 comprise samples ofcontrast agents having different densities or concentrations. Examplesof different contrast agents have been provided previously and thosecontrast agents (or others) can be used here. In general, during aprocedure, contrast agents flow through a blood vessel. Accordingly,this can be mimicked by placing the contrast agents as samples A1-A4,which are at the center of the normalization device. In someembodiments, one or more of the samples A1-A4 can be replaced with othersamples that may flow through a blood vessel, such as blood.

The samples B1-B4 can be selected to comprise samples that wouldgenerally be found on or around an inner blood vessel wall. For example,in some embodiments, one or more of the samples B1-B4 comprise samplesof calcium of different densities, and/or one or more of the samples ofB1-B4 comprise samples of fat of different densities. Similarly, thesamples C1-C4 can be selected to comprise samples that would generallybe found on or around an outer blood vessel wall. For example, in someembodiments, one or more of the samples C1-C4 comprise samples ofcalcium of different densities, and/or one or more of the samples ofC1-C4 comprise samples of fat of different densities. In one example,the samples B1, B3, and C4 comprise fat samples of different densities,and the samples B2, B4, C1, C2, and C3, comprise calcium samples ofdifferent densities. Other arrangements are also possible, and, in someembodiments, one or more of the compartments may hold other samples,such as, for example, air, tissue, radioactive contrast agents, gold,iron, other metals, distilled water, water, or others.

The embodiment of the normalization device 1500 of FIG. 15 , furtherillustrates several additional features that may be present in somenormalization devices. One such feature is represented by the differentsized compartments or volumes for the samples. For example, in theillustrated embodiment the sample B1 has a smaller volume than thesample B2. Similarly, the sample C4 has a volume that is larger than thesample C3. This illustrates that, in some embodiments, the volumes ofthe samples need to be all of the same size. In other embodiments, thevolumes of the samples may be the same size.

The embodiment of FIG. 15 also illustrates that various samples can beplaced adjacent to (e.g., immediately adjacent to or juxtaposed with)other samples. This can be important because, in some cases of medicalimaging, the radiodensity of one pixel may affect the radiodensity of anadjacent pixel. Accordingly, in some embodiments, it can be advantageousto configure the normalization device such that material samples thatare likely to be found in proximity to each other are similarly locatedin proximity to or adjacent to each other on the normalization device.The blood vessel-like arrangement of the normalization device 1500 mayadvantageously provide such a configuration.

In the illustrated embodiment, each sample A1-A4 is positioned so as tobe adjacent to two other samples A1-A4 and to two samples B1-B4. SamplesC1-C4 are each positioned so at to be adjacent to two other samplesC1-C4 and to a sample B1-B4. Although a particular configuration isillustrated, various other configurations for placing samples adjacentto one another can be provided. Although the normalization device 1500is illustrated within a plane, the normalization device 1500 will alsoinclude a depth dimension such that each of the samples A1-A4, B1-B4,and C1-C4 comprises a three-dimensional volume.

As noted previously, the normalization device can be calibratedspecifically for different types of medical imagers, as well as fordifferent types of diseases. The described embodiment of thenormalization device 1500 may be suitable for use with CT scans and forthe analysis of coronary conditions.

When configuring the normalization device for use with other types ofmedical imagers, the specific characteristics of the medical imager mustbe accounted for. For example, in an MRI machine, it can be important tocalibrate for the different depths or distances to the coils.Accordingly, a normalization device configured for use with MRI may havea sufficient depth or thickness that generally corresponds to thethickness of the body (e.g., from front to back) that will be imaged. Inthese cases, the normalization device can be placed adjacent to thepatient such that a top of the normalization device is positioned at thesame height as the patient’s chest, while the bottom of thenormalization device is positioned at the same height as the patient’sback. In this way, the distances between the patient’s anatomy and thecoils can be mirrored by the distances between the normalization deviceand the coils.

In some embodiments, the sample material can be inserted within tubespositioned within the normalization device.

As noted previously, in some embodiments, the normalization device maybe configured to account for various time-based changes. That is, inaddition to providing a three-dimensional (positional) calibration tool,the normalization device may provide four-dimensional (positional plustime) calibration tool. This can help to account for changes that occurin time, for example, as caused by patient movement due to respiration,heartbeat, blood flow, etc. To account for heartbeat, for example, thenormalization device may include a mechanical structure that causes itto beat at the same frequency as the patient’s heart. As another exampleof a time-based change, the normalization device can be configured tosimulate spreading of a contrast agent through the patient’s body. Forexample, as the contrast agent is injected into the body, a similarsample of contrast agent can be injected into or ruptured within thenormalization device, allowing for a time-based mirroring of the spread.

Accounting for time-based changes can be particularly important wherepatient images are captured over sufficiently large time steps that, forexample, cause the image to appear blurry. In some embodiments,artificial intelligence or other image-processing algorithms can be usedto reconstruct clear images from such blurry images. In these cases, thealgorithms can use the normalization device as a check to verify thatthe transformation of the image is successful. For example, if thenormalization device (which has a known configuration) appears correctlywithin the transformed image, then an assumption can be made that therest of the image has been transformed correctly as well.

Medical Reports Overview

Traditional reporting of medical information is designated for physicianor other provider consumption and use. Diagnostic imaging studies,laboratory blood tests, pathology reports, EKG readings, etc. are allinterpreted and presented in a manner which is often difficult tounderstand or even unintelligible by most patients. The text, data andimages from a typically report usually assumes that the reader hassignificant medical experience and education, or at least familiaritywith medical jargon that, while understandable by medical professionals,are often opaque to the non-medical layperson patient. To be concise,the medical reports do not include any sort of background educationalcontent and it assumes that the reader has formal medical education andunderstands the meaning of all of the findings in the report as well asthe clinical implications of those findings for the patient. Further,often findings are seen in concert with each other for specific diseasestates (e.g., reduced ejection fraction is often associated withelevated left ventricular volumes), and these relationships are nottypically reported as being as part of a constellation of symptomsassociated with a disease state or syndrome, so the non-medicallayperson patient cannot understand the relationship of findings tohis/her disease state.

It is then the responsibility and role of the medical provider to“translate” the reports into simple language which is typically verballycommunicated with the patient at the time of their encounter with theprovider be it in person or more recently during telehealth visits. Theprovider explains what the test does, how it works, what its limitationsmay be, what the patient’s results were and finally what those resultsmight mean for the patient’s future. Unfortunately, patients frequentlyare unable to fully interpret and retain all the information that theprovider might discuss with them in a short 10-15 typical patientencounter. The patients are then left confused and only partly educatedon the results of their medical reports. Often the provider will givethe patient a copy of the report both for their records as well as to beable to review on their own after the patient encounter.

Even with the patient report in hand and after hearing the physician’sexplanation, the patient often remains incompletely informed regardingthe results and their meaning. This can be a major source of frustrationfor both the provider as well as the patient. The patient does notunderstand fully the results of the study and their implications.Frequently patients will either reach out to friends and family to helpunderstand the results of their examination or they will performsearches on the Internet for additional background education andmeaning. Frequently however this is not successful as the patient maynot understand even what they are supposed to be searching for or askingabout the disease process and many online health information sites maybeinaccurate or misleading. All of this can impact current medical statusof the patient, his relation with the health provider, but also futurehealth implications including but not only therapeutic and futurediagnostic test adherence.

In response to this, providers sometimes refer patients to websites orprovide them with written materials that may help explain their testfindings and how this may relate to disease. But these are “generic”material that are not patient-specific, do not incorporate patientspecific findings, and do not relate to a patient’s specific conditionsor symptoms. To date, however, no methods have been devised or describedthat combines patient facing educational content as well as thepatient’s specific individual report findings in a way that can beeasily accessed, reviewed, and is available at the patient’s leisure forrepeated consumption as they may require. Thus, it is advantageous forsystems and methods that enable communication of these findings beyond asimple paper report by leveraging patient-specific information forgeneration of reports in the forms of more advanced and contemporarytechnology, such as movies, mixed reality or holographic environments.

Various aspects of systems and methods of generating a medical reportdataset and a corresponding medical report for a specific patient aredisclosed herein. In one example, a process includes receiving selectionof a report generation request, for a patient, for display on a displayof a computing system having one or more computer processors and one ormore displays, receiving patient information from a patient informationsource storing said patient information, the patient informationassociated with the report generation request, determining patientcharacteristics associated with the report generation request based onthe patient information, accessing a data structure storing associationsbetween patient characteristics and respective patient medicalinformation, medical images, and test results of one or more testperformed on the patient, and storing associations between patientcharacteristics and multimedia report data that is not related to aspecific patient, selecting from the data structure a report packageassociated with the patient medical information and the reportgeneration request, wherein the selected report package comprises apatient greeting in the language of the patient and presented by anavatar selected based on the patient data, a multimedia presentationconveying an explanation of the test performed, of the results of thetest, an explanation of the results of the test, and a conclusionsegment presented by the avatar, wherein at least a portion of themultimedia presentation includes report multimedia data from the reportdata source, test results from the results information source, medicalinformation from the medical information source, and medical imagesrelated to the test from the medical image source, automaticallygenerating the selected report package, and displaying the selectedreport package on the one or more displays, wherein the selected reportsare configured to receive input from a user of the computing system thatis usable in interacting with the selected parent report.

Systems for generating medical report can utilize existing patientmedical information, new images and test data, and/or contemporaneousinformation of the patient received from, for example, the medicalwearable device monitoring one or more physiological conditions orcharacteristics of the patient. Such systems can be configured toautomatically generate a desired report. In some embodiments, thesystems may use medical practitioner and/or patient interactive inputsto the determine certain aspects to include in the medical report. Inone example, a system for automatically generating a medical report caninclude a patient information source providing stored patientinformation patient information format, a medical information sourceproviding medical information in a medical information format, and amedical image source providing medical images in a medical image format.The medical images can be any images depicting a portion of a patient’sanatomy, for example, an arterial bed, one or more arterial beds. In anexample, an arterial bed includes arteries of one of the aorta, carotidarteries, lower extremity arteries, renal arteries, or cerebralarteries. The medical images can be any images depicting one or morearterial beds. In an example, a first arterial bed includes arteries ofone of the aorta, carotid arteries, lower extremity arteries, renalarteries, or cerebral arteries, and a second arterial bed includesarteries of one of the aorta, carotid arteries, lower extremityarteries, renal arteries, or cerebral arteries that are different thanthe arteries of the first arterial bed. In some embodiments, anormalization device (e.g., as described herein) is used when generatingthe medical images, and the information from the normalization device isused when processing the medical images. The medical images can beprocesses using any of the methods, processes, and/or systems describedherein, or other methods, processes, and/or systems. Any of the methodsdescribed herein can be based on imaging using the normalization deviceto improve quality of the automatic image assessment of the generatedimages. The system for automatically generating a medical report canalso include a test results information source providing test results ofone or more test performed on the patient in a results informationformat, a report data source, the report data source providingmultimedia data for including in a medical report, the multimedia dataindexed by at least some of the stored patient information relating tonon-medical characteristics of the patient, a report generationinterface unit to receive said patient information, the patientinformation including non-medical characteristics of a patient includingcharacteristics indicative of the patients age, gender, language, race,education level, and/or culture, and the like, wherein said reportgeneration interface unit can be adapted to automatically create medicalreport data links associated with said patient characteristics andassociated with report multimedia data on the report data source that isindexed by said respective patient characteristics based on a receivedreport generation request associated with the patient and a test, andwherein the report generation interface unit is further adapted toautomatically create links to patient information, medical information,medical images, and test results associated with the patient and thetest based on the report generation request. The system further includesa medical report dataset generator adapted to automatically access andretrieve the report multimedia data, patient information, medicalinformation, medical images, the test results using the medical reportdata links, and automatically generate a medical report associated withthe test and the patient based on the report multimedia data, patientinformation, medical information, medical images, the test results, themedical report conveying a patient greeting in the language of thepatient and presented by an avatar selected based on the patient data, amultimedia presentation conveying an explanation of the test performed,of the results of the test, an explanation of the results of the test,and a conclusion segment presented by the avatar, wherein at least aportion of the multimedia presentation includes report multimedia datafrom the report data source, test results from the results informationsource, medical information from the medical information source, andmedical images related to the test from the medical image source.

As described herein, one innovation relates to generating interactivemedical data reports. More particularly, the present applicationdescribes methods and systems for generating interactive coronary arterymedical reports that are optimized for interactive presentation andclearer understanding by the patient. One innovation includes a methodof generating a medical report of a medical test associated with one ormore patient tests. The method can include receiving an input of arequest of a medical report to generate for a particular patient, therequest indicating a selection of a format of the medical report, andreceiving patient information from a patient information source storingsaid patient information, where the patient information is associatedwith the report generation request. The method can include determiningpatient characteristics associated with the patient based on the patientinformation, and accessing one or more data structures storingassociations of types of medical reports, patient characteristics andrespective patient medical information, medical images, and test resultsof one or more test performed on the patient. The data structures arestructured to store associations between patient characteristics andmultimedia report data that is not related to a specific patient. Suchmethods can include accessing report content associated with thepatient’s medical information and the medical report request using theone or more data structures.

The content of the medical report can include multimedia contentincluding a greeting in the language of the patient, an explanationsegment of a type of test conducted, a results segment for conveyingtest results, an explanation segment explaining results of the test, anda conclusion segment, wherein at least a portion of the multimediacontent includes report data from the report data source, test resultsfrom the results information source, medical information from themedical information source, and medical images related to the test fromthe medical image source. Such methods can also include automaticallygenerating the requested medical report using the accessed reportcontent based at least in part on the selected format of the medicalreport. Such methods can also include displaying the medical report tothe patient. In some embodiments, the multimedia information furthercomprises data for generating and displaying an avatar on a display, theavatar being included in the medical report. In some embodiments, themethod further comprising generating the avatar based on one or morepatient characteristics. In some embodiments, the patientcharacteristics include one or more of age, race, and gender.

In some embodiments of such methods, a method can include displaying themedical report on one or more displays of a computer system, receivinguser input while the medical report can be displayed, and changing atleast one portion of the medical report based on said received userinput. In some embodiments, displaying the medical report comprisesdisplaying the medical report on the patient’s smart device. In someembodiments, the method includes storing the medical report. In someembodiments, the one or more data structures is configured to storeinformation representative of the severity of the patient’s medicalcondition, wherein selection of the content of the segments of themedical report are based on in part on the stored informationrepresentative of the severity of the patient’s medical condition.

Such methods can also include selecting a greeting segment for themedical report based on one or more of the patient’s race, age, gender,ethnicity, culture, language, education, geographic location, andseverity of prognosis. The method can also include selecting multimediacontent for the explanation segment based on one or more of thepatient’s race, age, gender, ethnicity, culture, language, education,geographic location, and severity of prognosis. The method can alsoinclude selecting multimedia content for the explanation of the resultssegment based on one or more of the patient’s race, age, gender,ethnicity, culture, language, education, geographic location, andseverity of prognosis. The method can also include selecting multimediacontent for the conclusion segment based on one or more of the patient’srace, age, gender, ethnicity, culture, language, education, geographiclocation, and severity of prognosis. In some embodiments, the one ormore data structures are configured to store associations related tonormality, risk, treatment type, and treatment benefit of medicalconditions, and wherein the method further includes automaticallydetermining normality, risk, treatment type, and treatment benefit toinclude in the report based on the patients test results, and the storedassociations related to normality, risk, treatment type, and treatmentbenefits. In some embodiments, the method can further include generatingan updated medical report based on a previously generated medicalreport, new test results, and an input by a medical practitioner.

Example System and Method for Automatically Generating Coronary ArteryMedical Data

Described herein are systems and methods for generating medical reportsthat provides an in-depth explanation of what the medical test orexamination was intended to look for, the results of the patient’sspecific medical findings, and what those findings may mean to thepatient. The medical reports can be automatically generated,understandable educational empowering movie of individualized adaptedpersonal aggregated medical information. As an example, a computerimplemented method of generating a multi-media medical report for apatient, the medical report associated with one or more tests of thepatient. One or more images used to determine information for themedical report, and/or one or more of the images used in the medicalreport, can be based on images generated using a normalization devicedescribed herein, the normalization device improving accuracy of thenon-invasive medical image analysis. In an example, a method comprisesreceiving an input of a request to generate the medical report for apatient, the request indicating a format for the medical report,receiving patient information relating to the patient, the patientinformation associated with the report generation request, determiningone or more patient characteristics associated with the patient usingthe patient information, accessing associations between types of medicalreports and patient medical information, wherein the patient medicalinformation includes medical images relating to the patient and testresults of one or more test that were performed on the patient, themedical images generated using the normalization device, and accessingreport content associated with the patient’s medical information and themedical report requested. The report content can include multimediacontent that is not related to a specific patient. For example, themultimedia content can include a greeting segment in the language of thepatient, an explanation segment explaining a type of test conducted, aresults segment for conveying test results, and an explanation segmentexplaining results of the test, and a conclusion segment, wherein atleast a portion of the multimedia content includes a test result and oneor more medical images that are related to a test performed on thepatient. The method can further include generating, based at least inpart on the format of the medical report, the requested medical reportusing the patient information and report content.

Certain components of certain embodiments of such systems and methodsare described herein. An example of cardiac CT study imaging in a singleexamination is provided.

1) Transform individual patient specific medical information into anunderstandable movie. This invention combines patient facing medicaleducation with patient specific medical results in a manner that has notbeen previously performed. While many online sites explain medicaldisease processes, they do not have the results of the patients’ medicaltests and the patients often do not know if they are even looking in theright area. By combining patient facing educational background as wellas specific analysis of their test results and meaning, the patientswill be educated in a manner that empowers them to make better healthdecisions. This approach can then combine additional materials beyondjust the present test findings, including additional information derivedfrom patient history, physical, clinical electronic medical record,wearable fitness and wellness trackers, patient-specific web browsersearch history and so on.

2) Provide an in-depth explanation of the test performed. To understandwhat the results of a test may be, patients must understand what thetest was intended to do, an explanation regarding how it works, as wellas the potential range of results, both normal and abnormal. Anexplanation of the test performed would include simple understandablemethods of what the test is intended to find and what the range ofpossibilities of the results may be. In the example provided a coronaryartery CT angiogram is intended to evaluate if there are blockages orplaque within the patient’s coronary arteries. In order to understandthe results, a patient needs to understand that the test is intended toevaluate the blood vessels that feed the heart muscle, that by injectingcontrast and doing CT images their coronary arteries can be evaluatedfor the presence of plaque and associated blockages. This understandingcan be conveyed to the patient using a patient’s actual images so thatthere is increased engagement and understanding.

3) Provide the results of the patient’s individual patient specificexamination. Having educated the patient regarding what test they had aswell as the range of all possible results, they are now better empoweredto understand what their specific results are in the context of therange of potential results from the examination. Combining the resultsof the patient’s findings with an explanation of what the test waslooking for enables the patient to better understand the meaning ofthose results. The patient’s individual results, whether they arequantitative values from a blood test, images and resultinginterpretation from a diagnostic imaging study such as CT, MRI,ultrasound etc., results from an ECG exam etc. Quantitative results,images, PDFs, or other results can be uploaded and presented within themovie.

4) Give explanations of the results. In addition to presenting theresults directly to the patient, an explanation of the meaning of theresults can then be presented simultaneously. This is performed usingdefined aggregation algorithms with previously recorded definitions anddiscussions of the range of results expected for an individual test. Forexample, in the case of the cardiac CT angiogram report, we will developshort explanations of the significance of the result of narrowing of ablood vessel. If there is no narrowing present then a short, animatedvideo discussion will explain that no narrowing was present and whatthat means, if there is a mild narrowing which is clinically defined asa narrowing between one and 24%, then a different video will be played.If the narrowing is between 24 and 49%, another video is played etc.Previously created video explanations of the range of expected resultswill have been created and are available to then be placed within thevideo depending on the individual results of the examination. In somecases, there may only be a binary result, and therefore only twoexplanations are necessary. In other cases, it may be many videosdepending on the initial test and the range of possible clinicallysignificant results. The patient specific results can sometimes even becompared to what would be expected to an average patient of the same ageand sex or to what age that result would be considered “average -normal”. Specifically, in this step, the patient’s test findings can belinked to clinical treatment or additional diagnostic recommendationsthat can be based upon professional societal practice guidelines orcontemporary research science, such as that derived from large-scaleregistries and trials. In this way, this approach can also beeducational to the medical professional and may allow for improved andcontemporary clinical decision support. This will allow for a shareddecision-making moment for the patient and the medical professional,without the need for them to read through scientific literature.

5) Use animation that is patient friendly and non-threatening. Theanimation selected for the video will be intended to be professional butfriendly and non-threatening to the patient in order to put them more atease and make them more open to hearing and understanding theexplanations. The animated physician or other explainer in the video canalso be matched to the patience sex, age, and race and even be presentedin the patient’s primary language. Alternatively, the patient’s owncountenance can be the patient within the video in a manner that is froma photography or, alternatively, rendered as a cartoon or avatar.

6) Can be delivered via web based and non-web-based methods. The methodof delivery to the patient can be via encrypted HIPAA compliantweb-based methods or non-web-based methods such as computer disks, otherstorage media, etc.

7) Can be viewed on computers, cell phones, and other devices. In thismanner, all patients will have access to the reports regardless of theirsocioeconomic status. Not all patients have access to the Internet, cellphones or other devices. Making it available on multiple media platformsincreases the degree of access.

8) Uses mixed reality for explanations. The use of advanced computergraphics an augmented or virtual reality may make some of theexplanations easier for the patients to understand. For example, avirtual reality trip into the body and through a blood vessel thendemonstrating the blood flow slowing down and/or stopping at the sightof a blockage will help the patient to understand the significance ofhaving that blockage in their body. Demonstrating the deployment of astent in that blood vessel at the sight of that blockage will then helpthe patient understand how their pathology may be treated and why. Thiscould also be done in a 3D/4D virtual reality manner; or as a hologram;or by other visual display. Similarly, this information can be conveyedby audio methods, such as a podcast or others.

9) Can be saved by the patient for future reference. The patientspecific movie containing an explanation of the test, their results andadditional information becomes property of the patient that they canstore for future use.

10) Can be compared to a normal reference population value. In somecases, there may be findings that, to maximize patient understanding,can be compared to normative reference values that are derived frompopulation-based cohorts or other disease cohorts. This may be providedin percentile, by age comparison (e.g., heart age versus biologicalage), or by visual display (e.g., on a bell-shaped curve or histogram).

11) Can be compared to prior studies. In some cases, the patient mayhave 2 studies (either the same test, e.g., CT-CT or different testsCT-ultrasound) that can be automatically compared for differences andreported as described above in #1-10. This will allow a patient tounderstand his/her progress over time in response to lifestyle ormedical therapy or interventional therapies. In other cases, the testfindings can be conveyed as in #1-10 as a function of heritability(e.g., from genomics or other ‘omics or family history), susceptibility(e.g., from lab markers over time, or from environmental lifestyleinsults, such as smoking).

12) Can be configured to communicate the likelihood of success. In somecases, the video generated will estimate the likelihood of success orfailure of any given intervention by calculating the likelihood throughrisk calculators or using clinical trial data or practice guidelines;and this can be reported in the movie.

Examples of Medical Report Generation Systems and Methods

FIG. 16 is a system diagram which shows various components of an exampleof a system 1600 for automatically generating patient medical reports,for example, patient medical reports based on CT scans and analysis,utilizing certain systems and methods described herein. Variousembodiments of such systems may include fewer components than is shownin FIG. 16 , additional components, or different components. In thisexample, the system 1600 includes an MRI scanner 16160, an ultrasoundscanner 1611, the CT scanner 1612, and other types of imaging devices1613. Information from scanners and imaging devices is provided to othercomponents of the system through one or more communication links 1601 orother communication mechanism for communicating information. Thecommunication link is also connected to other components the systemillustrated in FIG. 16 .

The system 1600 further includes archived patient medical informationand records 1602 which may have been collected in a variety of sourcesand over a period of time. The information and records may includepatient data 1604, patient results 1606, patient images 1608, (e.g.,stored images of CT scans, ultrasound scans, MRI scans, or other imagingdata.

The system 1600 further includes stored images 1614 (which may or maynot be patient related). The system 1600 further includes patientwearable information 1616 which may be collected one or more devicesworn by patient, devices sensing or measuring one or more types ofphysiological data or a characteristic of the patient, typically over aperiod of time. The system 1600 can further include laboratory data 1618(e.g., recent blood analysis results), and medical practitioner analysis1620 of any patient related data (e.g., images, laboratory data,wearable information, etc.). The system 1600 may communicate with othersystems and devices over a network 1650 which is in communication withcommunication links 1601.

System 1600 may further include a computing system 1622 which may beused perform any of the functionality related to communicating,analyzing, gathering, or viewing information on the system 1600. Thecomputing system 1622 can include a bus (not shown) that is coupled tothe illustrated components of the computing system 1622 (e.g., processor1624, memory 1628, display 1630, interfaces 1632, input/output devices1634, communication link 1601, and may also be coupled to othercomponents of the computing system 1622. The computing system 1622 mayinclude a processor 1624 or multiple processors for processinginformation and executing computer instructions. Hardware processor 1624may be, for example, one or more general purpose microprocessors.Computer system 1622 also includes memory (e.g., a main memory) 1628,such as a random-access memory (RAM), cache and/or other dynamic storagedevices, for storing information and instructions to be executed byprocessor 1624. Memory 1628 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 1624. Such instructions, whenstored in storage media accessible to processor 1624, render computersystem 1622 into a special-purpose machine that is customized to performthe operations specified in the instructions. The memory 1628 may, forexample, include instructions to allow a user to manipulate time-seriesdata to store the patient information and medical data, for example asdescribed in reference to FIGS. ’s 16 and 17 . The memory 1628 caninclude read only memory (ROM) or other static storage device(s) coupledin communication with the processor 1624 storing static information andinstructions for processor 1624. Memory 1628 can also include a storagedevice, such as a magnetic disk, optical disk, or USB thumb drive (Flashdrive), etc., coupled the processor 1628 and configured for storinginformation and instructions.

The computer system 1622 may be coupled via a bus to a display 1630, forexample, a cathode ray tube (CRT), light emitting diode (LED), or aliquid crystal display (LCD). The display may include a touchscreeninterface. The computing system 1622 may include an input device 1634,including alphanumeric and other keys, is coupled to bus forcommunicating information and command selections to processor 1622.Another type of user input device is cursor control, such as a mouse, atrackball, or cursor direction keys for communicating directioninformation and command selections to processor 1622 and for controllingcursor movement on display 1630. The input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Insome embodiments, the same direction information and command selectionsas cursor control may be implemented via receiving touches on a touchscreen without a cursor.

Computing system 1622 may include a user interface module 1632 toimplement a GUI that may be stored in a mass storage device as computerexecutable program instructions that are executed by the computingdevice(s). Computer system 1622 may further, implement the techniquesdescribed herein using customized hard-wired logic, one or more ASICs orFPGAs, firmware and/or program logic which in combination with thecomputer system causes or programs computer system 1622 to be aspecial-purpose machine. According to one embodiment, the techniquesherein are performed by computer system 1622 in response to processor(s)1624 executing one or more sequences of one or more computer readableprogram instructions contained in memory 1628. Such instructions may beread into memory 1628 from another storage medium. Execution of thesequences of instructions contained in the memory 1628 causesprocessor(s) 1624 to perform the process steps described herein. Inalternative embodiments, hard-wired circuitry may be used in place of orin combination with software instructions.

Various forms of computer readable storage media may be involved incarrying one or more sequences of one or more computer readable programinstructions to processor 1624 for execution. The instructions receivedby memory 1628 may optionally be stored before or after execution byprocessor 1624.

Computer system 1622 also includes a communication interface 1637coupled to other components of the computer system and to communicationlink 1601. Communication interface 1637 provides a two-way datacommunication coupling to a network link that is connected to acommunication link 1601. For example, communication interface 1637 maybe an integrated services digital network (ISDN) card, cable modem,satellite modem, or a modem to provide a data communication connectionto a corresponding type of telephone line. As another example,communication interface 1637 may be a local area network (LAN) card toprovide a data communication connection to a compatible LAN (or WANcomponent to communicate with a WAN). Wireless links may also beimplemented. In any such implementation, communication interface 1637sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

A network link typically provides data communication through one or morenetworks to other data devices. For example, a network link may providea connection through local network to a host computer or to dataequipment operated by an Internet Service Provider (ISP). An ISP in turnprovides data communication services through the worldwide packet datacommunication network now commonly referred to as the “Internet.”Computer system 1622 can send messages and receive data, includingprogram code, through the network(s), communication link 1601 andcommunication interface 1637. In the Internet example, a server mighttransmit a requested code for an application program through theInternet, ISP, local network communication link 1601, and acommunication interface. The received code may be executed by processor1624 as it is received, and/or stored in memory 1628, or othernonvolatile storage for later execution. The processor 1624, operatingsystem 1626, memory components 1628, one or more displays 1630, one ormore interfaces 1632, input devices 1634, and modules 1636, which may behardware or software, or a combination of hardware and software, thatwhen utilized performs functionality for the system. For example, themodules 1626 may include computer executable instructions that areexecuted by processor 1624 to perform the functionality of system 1600.

The system 1600 may further include medical report generation system1638 (“or medical report generator”) which can include variouscomponents that are used to generate medical report data set for aparticular patient for a requested type of report. Medical reportgeneration system 1638 may include a computing system, e.g., a server ora computing system 1640. In some embodiments, the computing system 1640includes a server. The medical report generation system also includescollected or determined patient specific information 1648, and a reporttemplate data structure 1642 which includes associations between apatient, the patient information 1648 (images, medical analysis and testresults associated with the patient), and report segments, reportelements, reports of elements for the desired. Medical report generationsystem 1638 further includes user parameters 1646 that may be specificto a medical practitioner and/or to a patient or entered by a medicalpractitioner and/or the patient.

The system 1600 may also include one or more computing devices 1652communication with the components of the system via a communicationlink(s) 1601. Communication link(s) 1601 may include wired and wirelesslinks. Computing device 1652 may be a tablet computer, laptop computer,a desktop computer, a smart phone, or another mobile device.

FIG. 17 is a block diagram that shows an example of data flow andfunctionality 1700 for generating the patient medical report based onone or more scans of the patient, patient information, medicalpractitioner’s analysis of the scans, and/or previous test results. Atthe beginning of this data flow new medical images 1702 are received bythe system or are generated by a scanner. The images can be generatedusing a normalization device described herein. Information derived fromimages generated and processed using the normalization device can bemore consistent and/or accurate, as described herein. The images can befrom a CT, MRI, ultrasound, or other type of scanner. The images depicta target feature of a patient’s body, for example, coronary arteries.The images may be archived in a patient medical information storagecomponent 1708, which stores other types of patient data (for example,previously generated images, patient test results, patient specificinformation that can include age, gender, race, BMI, medication, bloodpressure, heart rate, weight, height, body habitus, smoking, diabetes,hypertension, prior CAD, family history, lab test results, and thelike). The new images 1702 are provided for image analysis 1704, whichmay include analysis of the images using artificial intelligence /machine learning algorithms that have been trained to detect features incertain characteristics in the images. Other test 1706 may also havebeen conducted on the patient (e.g., blood work or another test).

The new images 1702, machine generated results to 1712, resultsdetermined by medical practitioners 1714, and previous test results 1716are collected in a results phase 1710, and this information iscommunicated to medical report data set generation block 1720. Otherpatient medical information 1718 can also be provided to medical reportdata set generation 1720. As indicated above, this information mayinclude, for example, a patient’s age, gender, race, BMI, medication,blood pressure, heart rate, weight, height, body habitus, smoking,diabetes, hypertension, prior CAD, family history, lab test results, andthe like. In addition to the results 1710 and the other patient medicalinformation 1718, medical report data set generation 1720 can alsoreceive report data 1728. Report data 1728 can include multimediainformation used for the report. For example, audio, images, sequencesof images (i.e., video), text, backgrounds, avatars, or anything elsefor the report that is not related to the specific patient’s medicalinformation.

Medical report data set generation 1720 can use the new images 1702, theresults 1710, other patient medical information 1718, and report data1728 to generate a medical report dataset for a requested type ofreport. The medical report data set generation 1770 can be interactive,and a medical practitioner can provide input identified what type ofreport is being generated. At block 1722, during the medical report dataset generation, all of the information that is needed for the requestedreport, is aggregated and the medical report is generated. For example,images, patient data, and other information needed for the report areidentified collected from the various inputs. At block 1724, the processuses certain patient information to tailor the report for the particularpatient. For example, one or more characteristics of an avatar thatpresents information in the report to the patient can be identified fromthe patient data such that the avatar is created to best convey reportinformation to the patient. In some examples, such information includesthe gender, age, language, education, culture, and the like,characteristics of the patient. At block 1726, the process determinesthe test explanation that is best used for the report. For example,there may be ten different explanations for a particular test, and oneof the ten explanations is selected for the report. The determination ofthe test explanation may be based on patient and/or the diagnosis orprognosis of results of the test. In other words, the same test may beexplained in various ways based on what the results of the test turnedout to be. At block 1728, the process determines results explanation.There can be multiple explanations for the same results, and one of theexplanations the selected port. The selection of the results explanationcan be based on, for example, patient information, the substance of theresults, or other information.

At block 1730, the process determines a greeting to be used in thereport. The greeting selected for the report may be one of numerouspossible greetings. In various embodiments, the greeting may be selectedbased on patient information, user input, or the results the test. Forexample, if the test results indicate great news for the patient, afirst type of greeting may be selected. If the test results areunfavorable to the patient, a second type of greeting may be selected ismore appropriate for subsequently delivered results.

At block 1732, the process determines the conclusion to be used in thereport. The conclusion selected for the report may be one of numerouspossible conclusions. In various embodiments, inclusion may be selectedbased on patient information, user input, or the results of the test.For example, the test results indicate great is for the patient thefirst type of the selected. The test results are unfavorable to thepatient, the second type of conclusion selected is more appropriate forthe previously reported unfavorable results.

The medical report data set generation 1720 provides a medical report1736. In some embodiments, the medical report is a video that includes apatient identification greeting 1738, and for each test, an explanationof the test 1740 results of the test 1742 and explanation of the results1744. For medical reports that include multiple tests, the report mayiteratively present a test explanation, present the results, and presentan explanation results for each test conducted. The medical report alsoincludes a conclusion segment 1746. In some embodiments, the medicalreport is displayed on the display to the patient/patient’s family. Insome embodiments, the medical report is provided as a video for thepatient to view at their home or anywhere else on a computer. In someembodiments, medical report can be provided is a paper copy.

FIG. 18A is a block diagram of an example of a first portion of aprocess for generating medical report using the functionality and datadescribed in reference to FIG. 17 , according to some embodiments. Atblock 1802, one or more medical tests are performed on a patient. Atblock 1804, results are generated by machine (e.g., a blood test), thetrain medical interpreter, and/or are automatically/semi-automaticallydetermined based on artificial intelligence/machine learning algorithms.At block 1806, results, patient information, and other data is collectedand sent to a computer device or network for creation of the medicalreport. At block 1808, results are aggregated with images, patientinformation, other data, multimedia information and the like to generatea medical related portion of report. At block 1810, the processgenerates the video presenter (e.g., an avatar) of the report usingcertain selected patient information, for example, biographical data ofthe patient. For example, when the patient is a child, patientinformation may be used to create child avatar which presents the reportto the child. In some embodiments, the child avatar may have been avatarpet which also helps present the report to the child, making the reportmore interesting and more fun for the child. When the patient is ahighly educated adult, patient information may be used to create anavatar that is appropriate to present the report to that patient. Insome embodiments, the avatar may mirror certain characteristics of thepatient (e.g., race, age, or gender) or be a determined complementaryavatar to certain characteristics of patient.

FIG. 18B is a block diagram of an example of a second portion of aprocess for generating medical report using the functionality and datadescribed in reference to FIG. 17 , according to some embodiments. Atblock 1812, the process selects a test explanation to be used for thereport. The selection of the test explanation can be based on thepatient information the severity of injury or disease, and/or theseriousness of the report (e.g., the final diagnosis). In one example, acertain test explanation may be selected from one of four testexplanation videos. At block 1814, the process selects explanationresults to be used for the report. The selection of the results can alsobe based on the patient information, severity of the injury or disease,and/or seriousness of report (e.g., the final diagnosis). In oneexample, the certain results explanation may be selected from one offour results explanation videos.

FIG. 18C is a block diagram of an example of a third portion of aprocess for generating medical report using the functionality and datadescribed in reference to FIG. 17 , according to some embodiments. Atblock 1816, the process selects patient identification greeting. Thereport and start with identification reading of the patient this mayinclude a cartoon character, or avatar, reading the patient by name andstating what test does been explained and when it was performed, whoordered the test and where it was performed. At block 1818, the processexplains the test conducted on the patient. A previously recordedsegment explains, for example, the patient what test was performed, howit works, why it is usually ordered by a provider, and what the range ofexpected results may be. At block 1820, the report then presents theresults to the patient. The results can include quantitative values,images, charts, videos, and other types of data that may help to conveythe results to the patient. At block 1822, the report may present adiscussion of results to help clarify to the patient exactly what theresults mean in some examples, appropriate prerecorded animation ofvideos explains the meaning of a result. If multiple tests wereperformed on the patient, the process may iteratively explain each test,present the test results, and then explain the results. At block 1824,the process presents a conclusion segment that may summarize informationfor the patient, provide additional information, and/or provide guidanceon the next steps taken by the patient or that will be taken by themedical practitioner. For all the parts of the report, medical reportgeneration functionality uses a combination of patient information,actual images and/or test results, and other multimedia information topresent a comprehensive clear explanation of each test that wasperformed in the results of the test.

FIG. 18D is a diagram illustrating various portions that can make up themedical report, and input can be provided by the medical practitionerand by patient information or patient input. As shown in FIG. 18D, themedical practitioner can interactively select a type of medical reportto be generated (e.g., report 1, report 2, etc.). Each medical report isa collection of data and information that can be collected and presentedin various segments of the report. For example, the segments can includea greeting, an explanation of the test(s) performed, results, anexplanation of the results, and a conclusion. Medical reports thatinclude multiple tests can include multiple segments that present anexplanation of each test performed, the results of each test, and anexplanation of the results of each test. In some embodiments, all orportions of the segment are automatically generated based on patientinformation, types of test performed, and the results of each test. Insome embodiments, the medical practitioner can select or proveinformation to use for each segment. In some embodiments, the report canbe interactive in a patient’s input can help determine what informationto use to generate a segment or present a portion of the report. Eachsegment may include a number of elements. Each of the elements caninclude one or more sub elements. For example, a segment of test resultsmay include an element for each of the test results to be included inthe report. In some embodiments, the medical practitioner can select orapprove of what information to use for an element and/or a sub-element.In some embodiments, the elements and/or the sub-elements can be atleast partially determined based on the patient information and/or thepatient input. Typically, the medical practitioner can interactivelyselect and/or approve of all material that is used in the report. Insome embodiments, contents of the report are based on predeterminedalgorithms that use the combination of patient information, medicaltests, medical results, and medical practitioners’ preferences todetermine the elements in each segment of the medical report.

FIG. 18E is a schematic illustrating an example of a medical reportgeneration data flow and communication of data used to generate areport. As illustrated components and data related to the components anddata illustrated in FIGS. 16-18D. A medical report generator 1850receives plurality of inputs which it uses to generate a particularmedical report for particular patient. This medical report is generatedto educate and inform a patient, and a patient’s caregivers, of aspecific patient’s medical tests and results. This medical reporting isa process that transforms individual medical information in anunderstandable movie. The movie is made with the patient’s avatar oravatar like (e.g., matched by sex, age ethnicity, etc.). Viewing of thereport can be done anywhere on a computer that a medical facility or ona patient’s computer (e.g., a smart phone, tablet, laptop, etc.). Reportmay contain multimedia data audio, text, images, and/or video. The videomay contain, cartoon, real life videos. Animation can include virtualreality for example video enters body, see heart pumping with bloodflowing, centered at vessels, see blood through vessels flowing andshowing plaque with changes in velocity and flowing - go to plaque andsee its distinct types. In some embodiments, augmented reality may beused to simulate, age, pharmacological changes, pharmacological agentsavailable where the exam is done, different degrees of disease, theeffect of interventions such as stents and bypass, behavior changes andexercise. The report may be shareable allowing a user able to share withanyone with a defined time of availability or forever. For example, itcan be transformed and condensed in a PDF, DICOM, or Word document, oranother format, for printing. The language used in the report can be thepatient’s native language. In some embodiments, subtitles can be usedfor hearing impaired in native language, or braille for the blind. Inembodiments using avatar, the avatar narration can be individualized forthe patient, to include age, gender, ethnicity - change in patient look,level of understanding - change in language and depth of information.

The medical report generator 1850 can receive input 1875 from a medicalpractitioner indicating to generate a particular type of report forparticular patient. In some embodiments, a medical practitioner canprovide inputs to determine certain aspects of the report. For example,the medical practitioner may indicate which image data to use in whichtest results to include in the report. In another example, the medicalpractitioner can, based on the test results and/or the severity of thediagnosis, the medical practitioner can influence the “tone” orseriousness of the report such that is appropriate for reporting thetest results in the diagnosis.

In some embodiments, the medical practitioner can provide inputs toapprove tentative automatically selected material to include in thereport. The medical report generator 1800 in communication with datastructures 1880 which store associations related to report generation.In some embodiments, the data structures 1880 include associationsbetween the particular medical practitioner and characteristics ofmedical reports that he prefers to generate. The associations may bedynamic and may interactively or automatically change over time. Thedata structures 1880 can also include associations that relate to all ofmaterial that can be used to generate a report. For example, after amedical practitioner indicates that a certain medical report generatedfor certain patient, the medical report generator 1880 receives patientinformation 1880 based on the associations data structures begins to itneeds to generate the medical report.

As illustrated in FIG. 18E, medical report generator 1850 can receivepre-existing portions of a report 1855 (segments, elements,sub-elements) that can include multi-media greetings, explanation of atest, presentation of results, explanation results, and conclusions.This material can be combined with other inputs the medical reportgenerator 1850 to generate the report. For example, the medical reportgenerator 1850 can receive patient information 1860 that includes thepatient’s age, gender, race, education, ethnicity, geographic location,in any other characteristic of pertinent information of the patientwhich may be used to tailor the medical report such that the informationin the medical report is best conveyed to the particular patient.Medical report generator 1850 can also receive image data 1862 relatedto recent test performed on the patient (e.g., CT, MRI, ultrasoundscans, or other image data), and/or previously collected image data 1865(e.g., previously collected CT, MRI, ultrasound scans, or other imagedata). For example, the previously collected image data 1865 can includeimage data that was taken over a period of time (for example, days,weeks, months, or years). The medical report generator 1850 can alsoreceive other medical data 1867 including but not limited to test,results, diagnosis of the patient. The medical report generator 1850 canalso receive multimedia report data 1870 which is used to form portionsof medical report. The multimedia report data 1870 can includeinformation relating to avatars, audio information, video information,images, and text that may be included in the report.

The medical report can apply to and /or discuss test results - imagingand non-imaging tests, and other medical information isolated oraggregated with or without therapeutic approach. For example, for agallstone surgery, the medical report can aggregate information from labtests, objective observation, medical history, imaging tests, includesurgery proposal, surgery explanation, virtual surgery, pathologicalfindings (more important in cancer), and explain after surgeryrecuperation until normal life or treatment FUP (ex: chemotherapy incancer). A medical report can also be educational, and generic andadapted to a patient, a disease, and/or a treatment, a test, and addressdisease, risk factors, treatment, behavior, and behavior changes. Someexamples, medical report can be generated to form part of a patient’scomplete electronic medical record (EMR) information. In some examples,the medical report generator 1850 can generate a comprehensive medicalreport per patient showing the patient “your medical life movie report.”

The medical report generator 1850 can be configured to generate themedical report in many different formats. For example, a movie,augmented reality, virtual reality, the hologram, a podcast (audioonly), a webcast (video), or for access using an interactive web-basedportal. In some embodiments, the information generated for the medicalreport can be stored in the data structures 1880 (e.g., the datastructures 1880 can be revised or updated to include information fromany of the inputs to the medical report generator 1850). In someembodiments, the medical report, or the information from the medicalreport stored in the data structures 1880 can be used to determineeligibility of the patient for additional trials test through an autocalculation feature. In such cases, the data structures 1880 areconfigured to store information that is needed for determining (orauto-calculating) such eligibility, including for example informationrelating to the patient’s age, gender, ethnicity, and/or race, wellness,allergies, pre-existing conditions, medical diagnosis, etc. In someexamples, information stored in the data structures 1880 can be used todetermine whether a patient fits inclusion criteria for large-scalerandomized trials, determine whether patient fit criteria forappropriate use criteria or professional societal guidelines (e.g.,AHA/ACC practice guidelines), determines whether patient’s insurancewill cover certain medications (e.g., statins vs. PCSK9 inhibitors), anddetermine whether a patient qualifies for certain employee benefits(e.g., exercise program). In some embodiments, the information used inthe data structures 1880 can be used to determine/indicate a patient’snormality, risk, treatment type and treatment benefits, and suchinformation can be included in the medical report, for example, based onmedical practitioners’ preferences. Accordingly, in various embodiments,in addition to the predetermined video/information 1855 relating togreetings, test explanations, results presented, results explanation,and conclusions, the medical report generator 1850 can be configured togenerate a medical report that includes information to help the medicalpractitioner explain the results and best way forward, the informationbeing based at least in part on the patient’s specific data (e.g., testdata), including:

-   a. patient-specific findings.-   b. comparison to normal values (age, gender, ethnicity,    race-specific values of population-based norms).-   c. comparison to abnormal values (e.g., comparing someone’s CAD    results to database of those who experienced heart attack; or    another database of similar).-   d. comparison to outcomes (e.g., identifying inclusion criteria for    trials and medication treatments therein, and auto-calculating    Kaplan Meier curves or other visual representations showing the    probability of an event respective time interval (e.g., survival    rate).-   e. comparison to identify benefits of treatment (e.g., auto-linking    to clinical trials or clinical data in order to examine the relative    benefits of specific types of treatment, e.g., medication therapy    with statins vs. PCSK9 inhibitors; medication treatment vs.    percutaneous intervention; PCI vs. surgical bypass).-   f. calculations of previously published (or unpublished) scores,    e.g., CONFIRM score, SYNTAX score, etc.-   g. comparisons from serial studies.-   h. auto-links to EMR or patient-entered data to enable    patient-specific explanation of medications and other treatments.-   i. can include “test” or “quiz” at the end to promote patient    engagement and ensure patient literacy.-   j. interactive patient satisfaction surveys.-   k. interactive with patients through patient input 1875, allowing a    patient to select which information they want to view and better    understand.-   1. ethnically, racially and gender diversity, and allow dynamic    changes in language, content based upon gender, race and ethnicity    that is used to convey report to patient; and-   m. adaptations for age allowing changes in language and content    based upon age, timeframe born (millennial vs. baby boomer).

In some embodiments, the medical report generator 1850 can be configuredto check for updates/received updates over time (e.g., auto-updating)such that the medical reports change over time and include the latestavailable reports. In some embodiments, the medical report generator1850 can communicate via a network or web-based portal to includeinformation from other medical or wearable devices. In some embodiments,the medical report generator 1850 can be configured to provide thepatient patient-specific education based upon published scientificevidence and specifically curated to the patient’s medical report, andauto-update the report based upon serial changes.

FIG. 18F is a diagram illustrating a representation of an example of asystem 1881 having multiple structures for storing and accessingassociated information that is used in a medical report, the informationassociated with a patient based on one or more of characteristics of thepatient, the patient’s medical condition, or an input from the patientand/or a medical practitioner. In some embodiments, the system 1881 is arepresentation of how the information used for generating a medicalreport is stored in systems of FIGS. 16, 17, or 18E. In FIG. 18F,information is described as being stored in a plurality of databases. Asused herein, a database refers to a way of storing information such thatthe information can be referenced by one or more values (e.g., otherinformation) associated with stored information. In various embodiments,a “database” can be, for example, a database, a data storage structure,a linked list, a lookup table, etc.). In some embodiments, the databasecan be configured to store structured information (e.g., information ofa predetermined size, for example, a name, age, gender, or otherinformation with a predetermined maximum field size). In someembodiments, database can be configured to store structured orunstructured information (e.g., information that may or may not bepredetermined, e.g., an image or a video). Stored information may beassociated with any other information of the patient. For example,stored information can be associated with one or more of acharacteristic of a patient (e.g., name, age, gender, ethnicity,geographic origin, education, weight, and/or height), one or moremedical conditions of a patient, a prognosis for a patient’s medicalcondition, medical treatments, etc. Although the example system 1881 inFIG. 18F illustrates having 13 different databases (e.g., for clarity ofthe description), in other embodiments such systems can have more orfewer databases, or certain information stored in illustrated databasescan be combined with other information and stored together in the samedatabase.

System 1881 includes a communication bus 1897, which allows thecomponents to communicate with each other, as needed. One or moreportions of the communication bus 1897 can be implemented as a wiredcommunication bus, or implemented as a wireless communication bus. Invarious embodiments, the communication but 1897 includes a plurality ofcommunication networks, or one or more types (e.g., a larger are network(LAN), a wide area network (WAN), the Internet, or a local wirelessnetwork (e.g., Bluetooth). System 1881 also includes a medical reportgenerator 1894, which is in communication with the communication bus1897. The medical report generator 1894 is also in communication withone or more input components 1895, which can be used for a patientand/or a medical practitioner to interface with the medical reportgenerator 1894 using a computer (e.g., a desktop computer, a laptopcomputer, a tablet computer, or a mobile device, e.g., a smart phone.

The medical report generator 1894 can communicate with any of thedatabases data structures using the communication bus 1897. In variousembodiments, medical report generator 1894 can use information from oneor more of the illustrated databases in a workflow, for generating apatient specific report, that includes patient identification, patientpreferences, medical image findings, patient diagnosis, prognostication,clinical decision making, health literacy, patient education, imagegeneration/display, and post-report education.

Patient identification is used by the medical report generator 1894 forgenerating an avatar that will be included in the medical report. Forexample, to be displayed during at least a portion of the medicalreport, or to be displayed and to “present” at least a portion of themedical report to the patient. Determining patient information can bebased upon either active or passive methods.

Passive

In some embodiments, a medical report generator 1894 can be configuredto automatically communicate with an electronic medical record (EMR)database 1893 to (for a certain patient) ascertain patient demographiccharacteristics to determine patient age, gender, ethnicity, and otherpotential relevant characteristics to understand patient biometrics(e.g., height, weight).

In some embodiments, the medical report generator 1894 can be configuredto automatically query a proprietary or web-based name origin database1883 containing names and ethnic origins of names to determine, whollyor in part, a patient’s gender and ethnicity based on the patient’s nameand/or other patient information.

Active

In some embodiments, the medical report generator 1894 can receive inputinformation from an interface system 1895, and the input information canbe used to generate portions of the medical report. For example, apatient, family/friend member, or medical professional can enter patientage, gender and ethnicity, and other potential relevant characteristics.This can be done, for example, at the time of receiving report and inadvance of playing the report; or at the time of registration of thepatient into the system.

In some embodiments, the medical report generator 1894 can receive apicture of the patient through an interface system 1895, or via thecommunication bus 1897, and the picture can be used to generate portionsof the medical report. For example, a picture of the patient can beinput into the system or be taken (e.g., input as an electronic image,or input by scanning in a photograph), and the picture can be used bythe medical report generator (or a system coupled to the medical reportgenerator) to automatically morph the picture into a relevant avatar(e.g., relevant to the patient). The determination of characteristics ofthe avatar can done using linked image-based algorithms that determineor choose an avatar from a repository of avatars that exist within thedata system, the avatar selected at least partially based on the pictureof the patient.

In some embodiments, a QR code can be used for all products related to acompany (e.g., Cleerly-related products) that can house informationabout the patient that can be used to generate the avatar.

Patient Preferences. In some embodiments, in this step the medicalreport generator 1902 can be configured to receive input from a patient,or a medical practitioner (e.g., via the interface system 1895) toidentify the ideal or desired educational method to maximize patientunderstanding of the medical report. In some embodiments, the systemgenerates graphical user interfaces (GUIs) that include options that canbe selected by a patient. In some embodiments, GUIs can include one ormore fields that a user (e.g., patient, medical practitioner, oranother) can enter data related to a preference (e.g., the length of thereport in minutes). Examples of inputs that can be received by a systemare illustrated below:

-   Method of delivery - The patient may choose to view their medical    report as a movie, in mixed reality (AR/VR), holography, podcast. In    other embodiments, the method of delivery is determined at least in    part by patient information.-   Length of report. Some patients are more detailed than other, and    would like more vs. less information. Patients can select the length    of their report (e.g., <5 minutes, 5-10 minutes, >10 minutes). In    other embodiments, the length of the report is determined    automatically at least in part using patient information.-   Popularity of report. If patients do not know what type of report    they want, the patients can select the “most popular” options. In    other embodiments, the type of report is determined automatically at    least in part using patient information.-   Effectiveness of the report. If patients do not have a preference of    what type of report they want, they can choose “most educational,”    which can be linked to report methods that have been demonstrated by    patient voting or by scientific study to maximize healthy literacy.    In other embodiments, the “effectiveness” of the report is    determined automatically at least in part based on patient    information.-   Report delivery voice. Patients can select what type of voice they    would like to hear for the report.

The medical report generator 1894 can also utilize a medical imagefindings database 1884 for the patient-specific medical report. Thereare a number of “medical image findings” that can be determined andstored in the medical image findings database 1884, and any one or moreof them can be incorporated into the medical report. The following aresome examples of the information that can be determined and stored inthe medical image findings database 1884.

Image processing algorithms process the heart and heart arteries from aCT scan to segment:

-   Coronary arteries - atherosclerosis, vascular morphology, ischemia-   Cardiovascular structures - left ventricular mass, left ventricular    volume, atrial volumes, aortic dimensions, epicardial fat, fatty    liver, valves

Heart and heart artery findings are quantified by, for example, thefollowing:

-   Coronary artery plaque - e.g., plaque burden, volume; plaque type,    percent atheroma volume, location, directionality, etc.-   Vascular morphology - e.g., lumen volume, vessel volume, arterial    remodeling, anomaly, aneurysm, bridging, dissection, etc.-   Left ventricular mass - in grams or indexed to body surface area or    body mass index-   Left ventricular volume - in ml or indexed to body surface area or    body mass index-   Atrial volumes - in ml or indexed to body surface area or body mass    index-   Aortic dimensions - in ml or indexed to body surface area or body    mass index-   Epicardial fat - in ml or indexed to body surface area or body mass    index-   Fatty liver - Hounsfield unit density alone or in relevance to    spleen

Quantified heart and heart artery findings are automatically sent to amedical image quantitative findings database 1885 that has well-definedareas for classification of each of these findings.

In some embodiments, the medical image quantitative findings database1885 has an algorithm that links together relevant findings thatcomprise syndromes over single disease states.

In an example, the presence of left ventricular volume elevation, alongwith the presence of left atrial volume elevation, along with thickeningof the mitral valve, along with a normal right atrial volume may suggesta patient with significant mitral regurgitation (or leaky mitral valve).

In another example, the presence of an increased aortic dimension andincreased left ventricular mass may suggest a person has hypertension.

The medical image quantitative findings database 1885 can link to otherelectronic data source (e.g., company database, electronic healthrecord, etc.) to identify potential associative relationships betweenstudy findings. For example, perhaps the electronic health recordindicates the patient has hypertension, in which case, the report willautomatically curate a health report card for patients specifically withhypertension, i.e., normality or left ventricular mass, atrial volume,ventricular volume, aortic dimensions.

The medical image quantitative findings database 1885 can link to theInternet to perform medical imaging finding-specific search (i.e.,search is based upon the image data curation as described above), toretrieve information that may link relevant findings that comprisesyndromes.

Diagnosis: morphologic classification of heart and heart arteryfindings:

Morphologic classification can be based upon:

Comparison to a population-based normative reference range database 1886which includes ranges that have the mean/95% confidence interval,median/interquartile interval; deciles for normality; quintiles ofnormality, etc. These data can also be reported in the medical report in“ages.” For example, perhaps a patient’s biological age is 50 years,while their heart age is 70 years based upon comparison to the age- andgender-based normative reference range database.

If the population-based normative reference range database 1886 does notexist in a system 1881, in some embodiments the system 1881 can searchthe Internet looking for these normative ranges, e.g., in PubMed searchand by natural language processing and “reading” of the scientificpapers.

Classification grades: can be done in many ways:

-   presence/absent-   normal, mild, moderate, severe-   elevated or reduced-   percentile for age, gender and ethnicity

Any of the above categorization systems, also accounting for otherpatient conditions (e.g., if a patient has hypertension, their expectedplaque volume may be higher than for a patient without hypertension).

Temporal / Dynamic changes can be done and integrated into the medicalreport by automatic comparison of findings with a patient’s prior studywhich exists in a specific prior exams database 1887, e.g., reportingthe change that has occurred, and direct comparison to thepopulation-based normative reference range database 1886 to determinewhether this change in disease is expectedly normal, mild, moderate,severe, etc. (or other classification grading method).

Temporal / Dynamic changes may be done by comparison of >2 studies(e.g., 4 studies) in the database of patient’s studies, in which changescan be reported by absolute, relative %, along a regression line, or byother mathematical transformation, with these findings compared to thepopulation-based normative reference range database.

Prognostication

Automatic prognostication of patient outcomes can be done by integratingthe medical imaging findings (± coupled to other patient data ± coupledto normative reference range database) by direct interrogation of aprognosis database 1888 that exists with patient-level outcomes. Theprognosis database 1888 may be a single database (e.g., of coronaryplaque findings), or multiple databases (e.g., one database for coronaryplaque, one database for ventricular findings, one database fornon-coronary vascular findings, etc.).

In some embodiments, several and separate databases may exist fordifferent types of prognosis, e.g., one database may exist forauto-calculation of risk of major adverse cardiovascular events (MACE),while another database may exist for auto-calculation of rapid diseaseprogression. These databases may be interrogated sequentially, or theymay be interactive with each other (e.g., a person who has a higher rateof rapid disease progression may also have a higher risk of MACE, butthe presence of rapid disease progression may increase risk of MACEbeyond that of someone who does not experience rapid diseaseprogression).

Prognostic findings can be reported into the movie report by:elevated/reduced; % risk, hazards ratio, time-to-event Kaplan Meiercurves, and others.

Clinical Decision Making

Automatic recommendation of treatments can be done by integrating theabove findings with a treatment database 1889. The treatment database1889 can house scientific and clinical evidence data to which apatient’s medical image findings, diagnosis, syndromes and prognosis canbe linked. Based upon these findings - as well as clinical trialinclusion / exclusion / eligibility criteria - a treatmentrecommendation can be given for a specific medication or procedure thatmay improve the patient’s condition.

For example, perhaps a patient had a specific amount of plaque on thepatient’s 1^(st) study and that plaque progressed significantly on thepatient’s 2^(nd) study. The system will report the change as high,normal, or low based upon query of the normative reference rangedatabase and the prior studies database and, based upon this, render aprognosis. The system could then query the EMR database to see whichmedications the patient is currently taking, and the system finds outthat the patient is taking a statin. The system could then examine thedatabases that would let the system know that adding a PCSK9 inhibitormedication on top of the statin medication would be associated with anXX% relative risk reduction. A similar example will be for a patientbeing considered for an invasive procedure.

In many cases, a treatment path is not 100% clear where there is benefitas well as risk for doing a specific kind of therapy. In this case, thesystem can query the shared decision database 1890, which lists thescientific evidence for treatment options, and lists all of the benefitsas well as limitations of these approaches. The “pros” and “cons” of thedifferent treatment approaches can be integrated into the patientmedical report.

For example, based upon the medical image findings, normative referencecomparison, prognosis evaluation and treatment query, perhaps an81-year-old woman would highly benefit from a medication whose sideeffect is worsening of osteoporosis. In this case, the woman may havesevere osteoporosis and for her, the benefits of the medication outweighthe risk as is illustrated and communicated through the shared decisionmaking database. For these types of cases, an alternative may beprovided.

For example, the shared decision making database may show comparativeeffectiveness of treatments, similar to the way Consumer Reports oramazon.com product options are listed so that the patient can understandthe options, pros and cons.

The system 1881 can also include a health literacy database 1891. Thisportion of the workflow to produce a medical report can be aninteractive “quiz” to ensure that the patient understood the studyfindings, the diagnosis, the prognosis, and the treatment decisionmaking. If the patient fails the “quiz”, then the system wouldautomatically curate content into more and more simple terms so that thepatient does understand their condition.

Thus, the health literacy database 1891 can be a tiered database ofmovies based upon simple to complex, and would be tailored to thepatient’s preferences as well as their score on the “quiz”. Thisinformation can be kept for future movies for that patient.

The opposite can also occur. As an example, perhaps a patient passes the“quiz” and the system asks the patient whether they would like to knowmore about the condition. If the patient answers ‘yes’, then the systemcan extract more and more complex movies for display to the patient. Inthis way, the health literacy database 1891 is multilevel andinteractive.

The system 1881 can also include an education database 1892, which haseducational materials that are based upon science and medicine, and areredundant in content but different in delivery method.

As an example, if the system notes that the patient has a certainfinding, the system can inquire with the patient whether they would liketo learn more about a specific conditions. If the patient indicates‘yes,’ then the system can inquire whether the patient would like to seea summary infographic page, a slide presentation, a movie, etc.

The system 1881 can also include an image display database 1893 thatincludes images that the medical report generator 1894 uses to morphmedical images into cartoon formats, or simpler formats, that a patientcan better understand.

The system can also include a post-report education database 1896 thatcontinually uploads new information in real time related to specificmedical conditions. The medical report generator 1894 can query thispost-report education database 1896, and curate educational content(e.g., scientific articles, publications, presentations, etc.) thatexist on the internet, and then modify them through the post-reporteducation database 1896 to information that the patient would like tosee, for example, as determined by the patient information or by a userinput.

The medical report generator 1894 system can be interactive, not justpassive. Different types of reports and information can be generated asa set of information for a medical report, and a user can interactivelyselect what information to view using the interface system 1895 (e.g., acomputer system of the user), and can select other information to bepresented/displayed by providing input to the medical report generator1894.

Systems and Methods for Imaging Methods of Non-Contiguous, or Different,Arterial Beds For Determining Atherosclerotic Cardiovascular Disease(ASCVD)

This portion of the disclosure relates to systems and methods forassessing atherosclerotic cardiovascular disease risk using sequentialnon-contiguous arterial bed imaging. Various embodiments describedherein relate to quantification and characterization of sequentialnon-contiguous arterial bed images to generate a ASCVD assessment, orASCVD risk score. Any risk score generated can be a suggested riskscore, and a medical practitioner can use the suggested ASCVD risk scoreto provide a ASCVD risk score for a patient. In various embodiments, asuggested ASCVD risk score can be used to provide a ASCVD risk score toa patient based on the suggested ASCVD risk score, or with additionalinformation.

In some embodiments, the ASCVD risk score is a calculation of your riskof having a cardiovascular problem over a duration of time, for example,1 year, 3 years, 5 years, 10 years, or longer). In some embodiments, thecardiovascular problem can include one or more of a heart attack orstroke. However, other cardiovascular problems can also be included,that is, assessed as a risk. In some embodiments, this risk estimateconsiders age, sex, race, cholesterol levels, blood pressure, medicationuse, diabetic status, and/or smoking status. In some embodiments, theASCVD risk score is given as a percentage. This is your chance of havingheart disease or stroke in the next 10 years. There are differenttreatment recommendations depending on your risk score. As an example,an ASCVD risk score of 0.0 to 4.9 percent risk can be considered low.Eating a healthy diet and exercising will help keep your risk low.Medication is not recommended unless your LDL, or “bad” cholesterol, isgreater than or equal to 190. An ASCVD risk score of 5.0 to 7.4 percentrisk can be considered borderline. Use of a statin medication may berecommended if you have certain conditions, which may be referred to as“risk enhancers.” These conditions may increase your risk of a heartdisease or stroke. Talk with your primary care provider to see if youhave any of the risk enhancers in the list below. An ASCVD risk score of7.5 to 20 percent risk can be considered intermediate. Typically for apatient with a score in this range, it is recommended that amoderate-intensity statin therapy is started. An ASCVD risk score ofgreater than 20 percent risk can be considered high. When the ASCVD riskscore indicates a high risk, it may be recommended that the patientstart a high-intensity statin therapy.

Various embodiments described herein also relate to systems and methodsfor quantifying and characterizing ASCVD of different arterial beds,e.g., from a single imaging examination. In some embodiments, thesystems and methods can quantify and characterize ASCVD of differentarterial beds from two or more imaging examinations. Any of the imagingperformed can be done in conjunction with a normalization device,described elsewhere herein. Various embodiments described herein alsorelate to systems and methods for determining an integrated metric toprognosticate ASCVD events by weighting findings from each arterial bed.Examples of systems and methods are described for quantifying andcharacterizing ASCVD burden, type and progression to logically guideclinical decision making through improved diagnosis, prognostication,and tracking of CAD after medical therapy or lifestyle changes. As such,some systems and methods can provide both holistic patient-level ASCVDrisk assessment, as well as arterial bed-specific ASCVD burden, type andprogression.

As an example relating to imaging of non-contiguous arterial beds thatis done in conjunction with a normalization device, a normalizationdevice is configured to normalize a medical image of a coronary regionof a subject for an algorithm-based medical imaging analysis. In anexample, a normalization device includes a substrate configured in sizeand shape to be placed in a medical imager along with a patient so thatthe normalization device and the patient can be imaged together suchthat at least a region of interest of the patient and the normalizationdevice appear in a medical image taken by the medical imager, aplurality of compartments positioned on or within the substrate, whereinan arrangement of the plurality of compartments is fixed on or withinthe substrate, and a plurality of samples, each of the plurality ofsamples positioned within one of the plurality of compartments, andwherein a volume, an absolute density, and a relative density of each ofthe plurality of samples is known. The plurality of samples can includea set of contrast samples, each of the contrast samples comprising adifferent absolute density than absolute densities of the others of thecontrast samples, a set of calcium samples, each of the calcium samplescomprising a different absolute density than absolute densities of theothers of the calcium samples, and a set of fat samples, each of the fatsamples comprising a different absolute density than absolute densitiesof the others of the fat samples. The set contrast samples can bearranged within the plurality of compartments such that the set ofcalcium samples and the set of fat samples surround the set of contrastsamples.

In an example, a computer implemented method for generating a riskassessment of atherosclerotic cardiovascular disease (ASCVD) uses anormalization device (as described herein) to improve accuracy of thealgorithm-based imaging analysis. In some embodiments, the medicalimaging method includes receiving a first set of images of a firstarterial bed and a first set of images of a second arterial bed, thesecond arterial bed being noncontiguous with the first arterial bed, andwherein at least one of the first set of images of the first arterialbed and the first set of images of the second arterial bed arenormalized using the normalization device, quantifying ASCVD in thefirst arterial bed using the first set of images of the first arterialbed, quantifying ASCVD in the second arterial bed using the first set ofimages of the second arterial bed, and determining a first ASCVD riskscore based on the quantified ASCVD in the first arterial bed and thequantified ASCVD in the second arterial bed. In some embodiments,determining a first weighted assessment of the first arterial bed basedon the quantified ASCVD of the first arterial bed and weighted adverseevents for the first arterial bed, and determining a second weightedassessment of the second arterial bed based on the quantified ASCVD ofthe second arterial bed and weighted adverse events for the secondarterial bed. Determining the first ASCVD risk score further comprisesdetermining the ASCVD risk score based on the first weighted assessmentand the second weighted assessment. In some embodiments, a method canfurther include receiving a second set of images of the first arterialbed and a second set of images of the second arterial bed, the secondset of images of the first arterial bed generated subsequent togenerating the first set of image of the first arterial bed, and thesecond set of images of the second arterial bed generated subsequent togenerating the first set of image of the second arterial bed,quantifying ASCVD in the first arterial bed using the second set ofimages of the first arterial bed, quantifying ASCVD in the secondarterial bed using the second set of images of the second arterial bed,and determining a second ASCVD risk score based on the quantified ASCVDin the first arterial bed using the second set of images, and thequantified ASCVD in the second arterial bed using the second set ofimages. Determining the second ASCVD risk score can be further based onthe first ASCVD risk score. In some embodiments, the first arterial bedincludes arteries of one of the aorta, carotid arteries, lower extremityarteries, renal arteries, or cerebral arteries. The second arterial bedincludes arteries of one of the aorta, carotid arteries, lower extremityarteries, renal arteries, or cerebral arteries that are different thanthe arteries of the first arterial bed. Any of the methods describedherein can be based on imaging using a normalization device to improvequality of the automatic image assessment of the generated images.

In an embodiment, an output of these methods can be a singlepatient-level risk score that can improve arterial bed-specificevent-free survival in a personalized fashion. In some embodiments, anyof the quantization of characterization techniques and processesdescribed in U.S. Pat. Application 17/142,120, filed Jan. 5, 2020,titled Systems, Methods, and Devices for Medical Image Analysis, RiskStratification, Decision Making and/or Disease Tracking″ (which isincorporated by reference herein), can be employed, in whole or in part,to generate a ASCVD risk assessment.

Traditional cardiovascular imaging using 3D imaging by computedtomography, magnetic resonance imaging, nuclear imaging or ultrasoundhave relied upon imaging single vascular beds (or territories) asregions of interest. Sometimes, multiple body parts may be imaged ifthey are contiguous, for example, chest-abdomen-pelvis CT, carotid andcerebral artery imaging, etc. Multi-body part imaging can be useful toidentify disease processes that affect adjoining or geographically closeanatomic regions. Multi-body part imaging can be used to enhancediagnosis, prognostication and guide clinical decision making oftherapeutic interventions (e.g., medications, percutaneousinterventions, surgery, etc.).

Additionally, methods that employ multi-body part imaging ofnon-contiguous arterial beds can be advantageous for enhancingdiagnosis, prognostication and clinical decision making ofatherosclerotic cardiovascular disease (ASCVD). ASCVD is a systemicdisease that can affect all vessel beds, including coronary arteries,carotid arteries, aorta, renal arteries, lower extremity arteries,cerebral arteries and upper extremity arteries. While historicallyconsidered as a single diagnosis, the relative prevalence, extent,severity and type of ASCVD (and its consequent effects on vascularmorphology) can exhibit very high variance between different arterialbeds. As an example, patients with severe carotid artery atherosclerosismay have no coronary artery atherosclerosis. Alternatively, patientswith severe coronary artery atherosclerosis may have milder forms oflower extremity atherosclerosis. As with the prevalence, extent andseverity, so too can the types of atherosclerosis differ amongstvascular beds.

A significant body of research now clarifies the clinical significanceof atherosclerotic cardiovascular disease (ASCVD) burden, type andprogression, as quantified and characterized by advanced imaging. As anexample, coronary computed tomographic angiography (CCTA) now allows forquantitative assessment of ASCVD and vascular morphology in all majorvascular territories. Several research trials have demonstrated that notonly the amount (or burden) of ASCVD, but also the type of plaque isimportant in risk stratification; in particular, low attenuation plaques(LAP) and non-calcified plaques which exhibit positive arterialremodeling are implicated in greater incidence of future major adversecardiovascular events (MACE); calcified plaques and, in particular,calcified plaques of higher density appear to be more stable. Somestudies that have evaluated this concept have been observational andwithin randomized controlled trials. Further, medication use has beenassociated with a reduction in LAP and an acceleration in calcifiedplaque formation in populations, with within-person estimates not yetreported. Medications such as statins, icosapent ethyl, and colchicinehave been observed by coronary computed tomography angiography (CCTA) tobe associated with modification of ASCVD in the coronary arteries.Similar findings relating the complexity or type of ASCVD in the carotidarteries has been espoused as an explanation for stroke, as well as forrenal arteries and lower extremity arteries.

Accordingly, understanding the presence, extent, severity and type ofASCVD in each of the vascular arterial beds improves understanding offuture risk of adverse cardiovascular events as well as the types ofadverse cardiovascular events that will occur (e.g., heart attack versusstroke versus amputation, etc.), and can allow tracking of the effectsof salutary medication and lifestyle modifications on the diseaseprocess in multiple arterial beds. Further, integrating the findingsfrom non-contiguous arterial beds into a single prediction model canimprove holistic assessment of an individual’s risk and response totherapy over time in a personalized, precision-based fashion. In someexamples, such assessments can include integrating an assessment ofcoronary arteries with an assessment of one or more other arterial beds,for example, one or more of the aorta, carotid arteries, lower extremityarteries, upper extremity arteries, renal arteries, and cerebralarteries. In some examples, such assessments can include integrating anassessment of any of the aorta, carotid arteries, lower extremityarteries, upper extremity arteries, renal arteries, or cerebral arterieswith a different one of the aorta, carotid arteries, lower extremityarteries, upper extremity arteries, renal arteries, or cerebralarteries.

Various embodiments described herein relate to systems and methods fordetermining assessments that may be used for reducing cardiovascularrisk and/or events. For example, such assessments can be used to, atleast partly, determine or generate lifestyle, medication and/orinterventional therapies based upon actual atheroscleroticcardiovascular disease (ASCVD) burden, ASCVD type, and/or and ASCVDprogression. In some embodiments, the systems and methods describedherein are configured to dynamically and/or automatically analyzemedical image data, such as for example non-invasive CT, MRI, and/orother medical imaging data of the arterial beds of a patient, togenerate one or more measurements indicative or associated with theactual ASCVD burden, ASCVD type, and/or ASCVD progression, for exampleusing one or more artificial intelligence (AI) and/or machine learning(ML) algorithms. The arterial beds can include for example, coronaryarteries, carotid arteries, and lower extremity arteries, renalarteries, and/or cerebral arteries. In some embodiments, the systems andmethods described herein can further be configured to automaticallyand/or dynamically generate assessments that can be used in one or morepatient-specific treatments and/or medications based on the actual ASCVDburden, ASCVD type, and/or ASCVD progression, for example using one ormore artificial intelligence (AI) and/or machine learning (ML)algorithms.

In some embodiments, the systems and methods described herein areconfigured to utilize one or more CCTA algorithms and/or one or moremedical treatment algorithms on two or more arterial bodies to quantifythe presence, extent, severity and/or type of ASCVD, such as for exampleits localization and/or peri-lesion tissues. In some embodiments, theone or more medical treatment algorithms are configured to analyze anymedical images obtained from any imaging modality, such as for examplecomputed tomography (CT), magnetic resonance (MR), ultrasound, nuclearmedicine, molecular imaging, and/or others. In some embodiments, thesystems and methods described herein are configured to utilize one ormore medical treatment algorithms that are personalized (rather thanpopulation-based), treat actual disease (rather than surrogate markersof disease, such as risk factors), and/or are guided by changes inCCTA-identified ASCVD over time (such as for example, progression,regression, transformation, and/or stabilization). In some embodiments,the one or more CCTA algorithms and/or the one or more medical treatmentalgorithms are computer-implemented algorithms and/or utilize one ormore AI and/or ML algorithms.

In some embodiments, the systems and methods are configured to assess abaseline ASCVD in an individual using two or more arterial bodies. Insome embodiments, the systems and methods are configured to evaluateASCVD by utilizing coronary CT angiography (CCTA). In some embodiments,the systems and methods are configured to identify and/or analyze thepresence, local, extent, severity, type of atherosclerosis, peri-lesiontissue characteristics, and/or the like. In some embodiments, the methodof ASCVD evaluation can be dependent upon quantitative imagingalgorithms that perform analysis of coronary, carotid, and/or othervascular beds (such as, for example, lower extremity, aorta, renal,and/or the like).

In some embodiments, the systems and methods are configured tocategorize ASCVD into specific categories based upon risk. For example,some example of such categories can include: Stage 0, Stage I, Stage II,Stage III; or none, minimal, mild, moderate; or primarily calcified vs.primarily non-calcified; or X units of low density non-calcifiedplaque); or X% of NCP as a function of overall volume or burden. In someembodiments, the systems and methods can be configured to quantify ASCVDcontinuously. In some embodiments, the systems and methods can beconfigured to define categories by levels of future ASCVD risk ofevents, such as heart attack, stroke, amputation, dissection, and/or thelike. In some embodiments, one or more other non-ASCVD measures may beincluded to enhance risk assessment, such as for example cardiovascularmeasurements (left ventricular hypertrophy for hypertension; atrialvolumes for atrial fibrillation; fat; etc.) and/or non-cardiovascularmeasurements that may contribute to ASCVD (e.g., emphysema). In someembodiments, these measurements can be quantified using one or more CCTAalgorithms.

In some embodiments, the systems and methods described herein can beconfigured to generate a personalized or patient-specific treatmentbased on an assessment of two or more arterial bodies. Morespecifically, in some embodiments, the systems and methods can beconfigured to generate therapeutic recommendations based upon ASCVDpresence, extent, severity, and/or type. In some embodiments, ratherthan utilizing risk factors (such as, for example, cholesterol,diabetes), the treatment algorithm can comprise and/or utilize a tieredapproach that intensifies medical therapy, lifestyle, and/orinterventional therapies based upon ASCVD directly in a personalizedfashion. In some embodiments, the treatment algorithm can be configuredto generally ignore one or more conventional markers of success—such aslowering cholesterol, hemoglobin A1C, etc.—and instead leverage ASCVDpresence, extent, severity, and/or type of disease to guide therapeuticdecisions of medical therapy intensification. In some embodiments, thetreatment algorithm can be configured to combine one or moreconventional markers of success—such as lowering cholesterol, hemoglobinA1C, etc., with ASCVD presence, extent, severity, and/or type of diseaseto guide therapeutic decisions of medical therapy intensification. Insome embodiments, the treatment algorithm can be configured to combineone or more novel markers of success—such as genetics, transcriptomics,or other ‘omic measurements—with ASCVD presence, extent, severity,and/or type of disease to guide therapeutic decisions of medical therapyintensification. In some embodiments, the treatment algorithm can beconfigured to combine one or more other imaging markers of success—suchas carotid ultrasound imaging, abdominal aortic ultrasound or computedtomography, lower extremity arterial evaluation, and others—with ASCVDpresence, extent, severity, and/or type of disease to guide therapeuticdecisions of medical therapy intensification.

In some embodiments, the systems and methods are configured to updatepersonalized treatment based upon response assessment of two or morearterial bodies. In particular, in some embodiments, based upon thechange in ASCVD between the baseline and follow-up CCTA, personalizedtreatment can be updated and intensified if worsening occurs orde-escalated / kept constant if improvement occurs. As a non-limitingexample, if stabilization has occurred, this can be evidence of thesuccess of the current medical regimen. Alternatively, as anothernon-limiting example, if stabilization has not occurred and ASCVD hasprogressed, this can be evidence of the failure of the current medicalregimen, and an algorithmic approach can be taken to intensify medicaltherapy.

To facilitate an understanding of the systems and methods discussedherein, several terms are described below. These terms, as well as otherterms used herein, should be construed to include the provideddescriptions, the ordinary and customary meanings of the terms, and/orany other implied meaning for the respective terms, wherein suchconstruction is consistent with context of the term. Thus, thedescriptions below do not limit the meaning of these terms, but onlyprovide example descriptions.

Presence of ASCVD: This can be the presence vs. absence of plaque; orthe presence vs. absence of non-calcified plaque; or the presence vs.absence of low attenuation plaque

Extent of ASCVD: This can include the following:

-   Total ASCVD Volume-   Percent atheroma volume (atheroma volume / vessel volume × 100)-   Total atheroma volume normalized to vessel length (TAVnorm).-   Diffuseness (% of vessel affected by ASCVD)

Severity of ASCVD: This can include the following:

-   ASCVD severity can be linked to population-based estimates    normalized to age, gender, ethnicity, and/or CAD risk factors-   Angiographic stenosis ≥70% or ≥50% in none, 1-, 2-, or 3-VD

Type of ASCVD: This can include the following:

-   Proportion (ratio, %, etc.) of plaque that is non-calcified vs.    calcified-   Proportion of plaque that is low attenuation non-calcified vs.    non-calcified vs. low density calcified vs. high-density calcified-   Absolute amount of non-calcified plaque and calcified plaque-   Absolute amount of plaque that is low attenuation non-calcified vs.    non-calcified vs. low density calcified vs. high-density calcified-   Continuous grey-scale measurement of plaques without ordinal    classification-   Vascular remodeling imposed by plaque as positive remodeling (≥1.10    or ≥1.05 ratio of vessel diameter / normal reference diameter; or    vessel area / normal reference area; or vessel volume / normal    reference volume) vs. negative remodeling (≤1.10 or ≤1.05)-   Vascular remodeling imposed by plaque as a continuous ratio

ASCVD Progression

-   Progression can be defined as rapid vs. non-rapid, with thresholds    to define rapid progression (e.g., >1.0% percent atheroma    volume, >200 mm3 plaque, etc.)-   Serial changes in ASCVD can include rapid progression, progression    with primarily calcified plaque formation; progression with    primarily non-calcified plaque formation; and regression.

Categories of Risk

-   Stages: 0, I, II, or III based upon plaque volumes associated with    angiographic severity (none, non-obstructive, and obstructive 1VD,    2VD and 3VD)-   Percentile for age and gender and ethnicity and presence of risk    factor (e.g., diabetes, hypertension, etc.)-   % calcified vs. % non-calcified as a function of overall plaque    volume-   X units of low density non-calcified plaque-   Continuous 3D histogram analysis of grey scales of plaque by lesion,    by vessel and by patient-   Risk can be defined in a number of ways, including risk of MACE,    risk of angina, risk of ischemia, risk of rapid progression, risk of    medication non-response, etc.

Certain features in embodiments of systems and methods relating todetermining an assessment of non-contiguous arterial beds are describedbelow.

Medical Imaging of Non-Contiguous Arterial Beds

Systems and methods described herein also relate to medical imaging ofnon-contiguous arterial beds. For example, imaging of non-contiguousarterial beds in a single imaging examination. In other embodiments,imaging of non-contiguous arterial beds in two or more imagingexaminations, and the information from the generated images can be usedto determine information relating to a patient’s health. As an example,coronary artery and carotid arteries are imaged using the same contrastbolus. In this case, the coronary arteries can be imaged by CCTA.Immediately after CCTA image acquisition, the CT table moves and imagesthe carotid artery using the same or supplemental contrast dose. Theexample here is given for CT imaging in a single examination, but can bealso applied to combining information from multiple imagingexaminations; or multimodality imaging integration (e.g., ultrasound ofthe carotid; computed tomography of the coronary).

Automated Arterial Bed-Specific Risk Assessment

This is accomplished by an automated method for quantification andcharacterization of ASCVD in individual artery territories for improveddiagnosis, prognostication, clinical decision making and tracking ofdisease changes over time. These findings may be arterial bed-specific.As an example, conversion of non-calcified plaque to calcified plaquemay be a feature that is considered beneficial and a sign of effectivemedical therapy in the coronaries, but may be considered to be apathologic process in the lower extremity arteries. Further, theprognostication enabled by the quantification and characterization ofASCVD in different artery territories may differ. As an example,untoward findings in the carotid arteries may prognosticate futurestroke; while untoward findings in the coronary arteries may prognosticfuture myocardial infarction. Partial overlap of risk may occur, e.g.,wherein adverse findings in the carotid arteries may be associated withan increase in coronary artery events.

Patient-Specific Risk Assessment

By combining the findings from each arterial bed, along with relativeweighting of arterial bed findings, risk stratification, clinicaldecision making and disease tracking can be done with greater precisionin a personalized fashion. Thus, patient-level prediction models arebased upon understanding the ASCVD findings of non-contiguous arterialbeds but communicated as a single integrated metric (e.g., 1-100,mild/moderate/severe risk, etc.).

Longitudinal Updating of Arterial Bed- and Patient-Specific Risk

By longitudinal serial imaging after treatment changes (e.g.,medication, lifestyle, and others), changes in ASCVD can be quantifiedand characterized and both arterial bed-specific as well aspatient-level risk can be updated based upon the changes as well as themost contemporary ASCVD image findings.

FIG. 19A illustrates an example of a process 1900 for determining a riskassessment using sequential imaging of noncontiguous arterial beds ofpatient, according to some embodiments. At block 1905, sequentialimaging of noncontiguous arterial beds of a patient may be performed. Anexample, sequential imaging be noncontiguous first arterial bed secondarterial bed performed. In some embodiments, the first arterial bedincludes one of aorta, carotid arteries, lower extremity arteries, upperextremity arteries, renal arteries, or cerebral arteries, and the secondarterial bed includes a different one of aorta, carotid arteries, lowerextremity arteries, upper extremity arteries, renal arteries, orcerebral arteries. In some embodiments the third arterial bed may beimaged. In some embodiments a fourth arterial bed may be imaged. Thethird and fourth arterial beds may include one of aorta, carotidarteries, lower extremity arteries, upper extremity arteries, renalarteries, or cerebral arteries. The sequential imaging of thenoncontiguous arterial beds may be done the same settings on the imagingmachine, at different times, or with different imaging modalities, forexample, CT and ultrasound).

At block 1910, the process 1900 automatically quantifies andcharacterizes ASCVD in the imaged arterial beds. In some embodiments,the ASCVD in the first arterial bed and the second arterial bed arequantified and characterized using any of the qualifications andcharacterization disclosed herein. For example, images of the firstarterial bed are analyzed by a system configured with a machine learningprogram that has been trained on a plurality of arterial bed images andannotated features of arterial bed images. In other embodiments, theASCVD and the first arterial bed and second arterial bed are quantifiedusing other types of qualifications the characterizations.

At block 1915, the process 1900 generates a prognostic assessment ofarterial bed specific adverse events. An example, for the coronaryarteries the adverse event can be a heart attack. In another example,for the carotid arteries the adverse event is a stroke. In anotherexample, for the lower extremity arteries the adverse event isamputation. The adverse events can be determined from patientinformation that is accessible to the system performing the assessment.For example, from archived patient medical information (e.g., patientmedical information 1602 illustrated in FIG. 16 ) or any other storedinformation of a previous adverse event. Each event can be associatedwith a weight based on a predetermined scheme. The weights can be, forexample, a value between 0.00 and 1.00. The weights associated withdifferent adverse events can be stored in a non-transient storagemedium, for example, a database. For a patient, a weighted assessment ofeach particular occurrence of an adverse event can be determined. Insome embodiments, the weights are multiplied by the event. For example,for a 1^(st) occurrence of event 1 that has a weight of 0.05, oneoccurrence of that event results in a weighted assessment of 00.05. Asecond occurrence of event 1 may have the same weight, or a differentweight. For example, an increased weight. In one example, a secondoccurrence of event 1 has a weight of 0.15, such that when twooccurrences of the event occur the weighted assessment is the sum of theweights of the first and second occurrence (for example, 0.20). Otherevents can have difference weights, and the weighted assessment caninclude the sum of all of the weights for all of the events thatoccurred.

At block 1920, the process 1900 uses the arterial bed specific riskassessment to determine a patient level risk score, for example, anASCVD risk score. In an example, the ASCVD risk score is based on aweighted assessment of an arterial bed. In an example, the ASCVD riskscore is based on a weighted assessment of an arterial bed and otherpatient information.

At block 1925, the process 1900 tracks changes in ASCVD based upontreatment and lifestyle to determine beneficial or adverse changes inASCVD. In some embodiments, as indicated in block 1930, the process 1900uses additional sequential imaging, taken at a different time (e.g.,days, weeks, months or years later) of one or more noncontiguousarterial beds and the process 1900 updates arterial bed and patientlevel risk assessments, and determines an updated ASCVD score based onthe additional imaging. The baseline and updated assessment can alsointegrate non-imaging findings that are associated with arterial bed-and patient-specific risk. These may include laboratory tests (e.g.,troponin, b-type natriuretic peptide, etc.); medication type, dose andduration (e.g., lovastatin 20 mg per day for 6 years); interactionsbetween multiple medications (e.g., lovastatin alone versus lovastatinplus ezetimibe); biometric information (e.g., heart rate, heart ratevariability, pulse oximetry, etc.) and patient history (e.g., symptoms,family history, etc.). By monitoring the ASCVD score and correlatingchanges in the ASCVD score with patient treatment(s) and patientlifestyle changes, better treatment protocols and lifestyle choices forthat patient may be determined.

FIG. 19B illustrates an example where a sequential, non-contiguousarterial bed imaging is performed. In this example, a sequential,non-contiguous arterial bed imaging is performed for the (1) coronaryarteries, and for the (2) carotid arteries. As can be seen in thequantification and characterization of the atherosclerosis in both thecoronary and carotid arteries, the phenotypic makeup of the diseaseprocess is highly variable, with the coronary artery cross-sectionsshowing both blue (calcified) and red (low-density non-calcified)plaque; and the carotid artery cross-sections only showing yellow(non-calcified) and red (low-density non-calcified plaque). Further, theamount of atherosclerosis is higher in the coronary arteries than thecarotid arteries, indicating a differential risk of heart attack andstroke, respectively.

FIG. 19C is an example of a process 1950 for determining a riskassessment of atherosclerotic cardiovascular disease (ASCVD) usingsequential imaging of non-contiguous arterial beds, according to someembodiments. At block 1952 a first arterial bed of a patient is imaged.In some embodiments, the first arterial bed includes one of an aorta,carotid arteries, lower extremity arteries, upper extremity arteries,renal arteries, or cerebral arteries. In some embodiments, the imagingused can be digital subtraction angiography (DSA), duplex ultrasound(DUS), computed tomography (CT), magnetic resonance angiography (MRA),ultrasound, or magnetic resonance imaging (MRI), or another type ofimaging that generates a representation of the arterial bed. At block1954 the process 1950 images a second arterial bed. The imaging of thesecond arterial bed is noncontiguous with the first arterial bed. Insome embodiments, the second arterial bed can be one of an aorta,carotid arteries, lower extremity arteries, upper extremity arteries,renal arteries, or cerebral arteries in his different than the firstarterial bed. In some embodiments, imaging the second arterial bed canbe performed by a DSA, DUS, CT, MRA, ultrasound, or MRI imaging process,or another imaging process. At block 1956 the process 1950 automaticallyquantifies ASCVD in the first arterial bed. At block 1958, the process1950 automatically quantifies ASCVD in the second arterial bed. Thequantification of ASCVD in the first arterial bed and the secondarterial bed can be done using any of the quantification disclosedherein (e.g., using a neural network trained with annotated images) orother quantification.

At block 1960, the process 1950 determines a first weighted assessmentof the first arterial bed, the first weighted assessment associated witharterial bed specific adverse events for the first arterial bed. Atblock 1962 the process 1950 determines a second weighted assessment ofsecond arterial bed, the second weighted assessment associated witharterial bed specific adverse events for the second arterial bed. Atblock 1964 the process 1950 generates an ASCVD patient risk score basedon the first weighted assessment and the second weighted assessment.

FIG. 19D is an example of a process 1970 for determining a riskassessment using sequential imaging of non-contiguous arterial beds,according to some embodiments. At block 1972, the process 1970 receivesimages of the first arterial bed and a second arterial bed, the secondarterial bed being noncontiguous with the first arterial bed anddifferent than the first arterial bed. In some embodiments, the firstarterial bed can be one of an aorta, carotid arteries, lower extremityarteries, upper extremity arteries, renal arteries, or cerebralarteries. The imaging of the second arterial bed is noncontiguous withthe first arterial bed. In some embodiments, the images of the firstarterial bed were generated by a DSA, DUS, CT, MRA, ultrasound, or MRIimaging process, or another imaging process. In some embodiments, theimages of the second arterial bed were generated by a DSA, DUS, CT, MRA,ultrasound, or MRI imaging process, or another imaging process. In someembodiments, the second arterial bed can be one of an aorta, carotidarteries, lower extremity arteries, upper extremity arteries, renalarteries, or cerebral arteries, and is different than the first arterialbed. In some embodiments, the images of the first arterial bed and thesecond arterial bed may be received from a computer storage medium thatis configured to store patient images. In some embodiments, the imagesof the first arterial bed and the second arterial bed may be receiveddirectly from a facility which generates the images. In someembodiments, the images of the first arterial bed and second arterialbed may be received indirectly from a facility which generates theimages. In some embodiments, images of first arterial bed may bereceived from a different source than images of second arterial bed.

At block 1974 the process 1970 automatically quantifies ASCVD in thefirst arterial bed. At block 1976, the process 1970 automaticallyquantifies ASCVD in the second arterial bed. The quantification of ASCVDin the first arterial bed and the second arterial bed can be done usingany of the quantification disclosed herein, or other quantification.

At block 1978 the process 1970 determines a first weighted assessment ofthe first arterial bed, the first weighted assessment associated witharterial bed specific adverse events for the first arterial bed. Atblock 1980 the process 1970 determines a second weighted assessment ofsecond arterial bed, the second weighted assessment associated witharterial bed specific adverse events for the second arterial bed. Atblock 1982 the process 1970 generates an ASCVD patient risk score basedon the first weighted assessment and the second weighted assessment.

FIG. 19E is a block diagram depicting an embodiment of a computerhardware system 1985 configured to run software for implementing one ormore embodiments of systems and methods for determining a riskassessment using sequential imaging of noncontiguous arterial beds of apatient. In some embodiments, the systems, processes, and methodsdescribed herein are implemented using a computing system, such as theone illustrated in FIG. 19E. The example computer system 1985 is incommunication with one or more computing systems 1994 and/or one or moredata sources 1995 via one or more networks 1993. While FIG. 19Eillustrates an embodiment of a computing system 1985, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 1985 can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 1985 can comprise a Quantification, Weighting, andAssessment Engine 1991 that carries out the functions, methods, acts,and/or processes described herein. For example, in some embodiments thefunctions of blocks 1956, 1958, 1960, 1962, and 1964 of FIG. 19C. Insome embodiments, the functions of blocks 1972, 1974, 1976, 1978, 1980,and 1982 of FIG. 19D. The Quantification, Weighting, and AssessmentEngine 1991 is executed on the computer system 1985 by a centralprocessing unit 1989 discussed further below.

In general the word “engine,” as used herein, refers to logic embodiedin hardware or firmware or to a collection of software instructions,having entry and exit points. Such “engines” may also be referred to asa module, and are written in a program language, such as JAVA, C, orC++, or the like. Software modules can be compiled or linked into anexecutable program, installed in a dynamic link library, or can bewritten in an interpreted language such as BASIC, PERL, LAU, PHP orPython and any such languages. Software modules can be called from othermodules or from themselves, and/or can be invoked in response todetected events or interruptions. Modules implemented in hardwareinclude connected logic units such as gates and flip-flops, and/or caninclude programmable units, such as programmable gate arrays orprocessors.

Generally, the modules described herein refer to logical modules thatcan be combined with other modules or divided into sub-modules despitetheir physical organization or storage. The modules are executed by oneor more computing systems, and can be stored on or within any suitablecomputer readable medium, or implemented in-whole or in-part withinspecial designed hardware or firmware. Not all calculations, analysis,and/or optimization require the use of computer systems, though any ofthe above-described methods, calculations, processes, or analyses can befacilitated through the use of computers. Further, in some embodiments,process blocks described herein can be altered, rearranged, combined,and/or omitted.

The computer system 1985 includes one or more processing units (CPU,GPU, TPU) 1989, which can comprise a microprocessor. The computer system1985 further includes a physical memory 1990, such as random accessmemory (RAM) for temporary storage of information, a read only memory(ROM) for permanent storage of information, and a mass storage device1986, such as a backing store, hard drive, rotating magnetic disks,solid state disks (SSD), flash memory, phase-change memory (PCM), 3DXPoint memory, diskette, or optical media storage device. Alternatively,the mass storage device can be implemented in an array of servers.Typically, the components of the computer system 1985 are connected tothe computer using a standards-based bus system. The bus system can beimplemented using various protocols, such as Peripheral ComponentInterconnect (PCI), Micro Channel, SCSI, Industrial StandardArchitecture (ISA) and Extended ISA (EISA) architectures.

The computer system 1985 includes one or more input/output (I/O) devicesand interfaces 1988, such as a keyboard, mouse, touch pad, and printer.The I/O devices and interfaces 1988 can include one or more displaydevices, such as a monitor, that allows the visual presentation of datato a user. More particularly, a display device provides for thepresentation of GUIs as application software data, and multi-mediapresentations, for example. The I/O devices and interfaces 1988 can alsoprovide a communications interface to various external devices. Thecomputer system 1985 can comprise one or more multi-media devices 1985,such as speakers, video cards, graphics accelerators, and microphones,for example.

Computing System Device / Operating System

The computer system 1985 can run on a variety of computing devices, suchas a server, a Windows server, a Structure Query Language server, a UnixServer, a personal computer, a laptop computer, and so forth. In otherembodiments, the computer system 1985 can run on a cluster computersystem, a mainframe computer system and/or other computing systemsuitable for controlling and/or communicating with large databases,performing high volume transaction processing, and generating reportsfrom large databases. The computing system 1985 is generally controlledand coordinated by an operating system software, such as z/OS, Windows,Linux, UNIX, BSD, PHP, SunOS, Solaris, MacOS, ICloud services or othercompatible operating systems, including proprietary operating systems.Operating systems control and schedule computer processes for execution,perform memory management, provide file system, networking, and I/Oservices, and provide a user interface, such as a graphical userinterface (GUI), among other things.

Network

The computer system 1985 illustrated in FIG. 19E is coupled to a network1993, such as a LAN, WAN, or the Internet via a communication link 1992(wired, wireless, or a combination thereof). Network 1993 communicateswith various computing devices and/or other electronic devices. Network1993 is communicating with one or more computing systems 1994 and one ormore data sources 1995. For example, the computer system 1985 canreceive image information (e.g., including images of arteries or anarterial bed, information associated to the images, etc.) from computingsystems 1994 and/or data source 1995 via the network 1993 and store thereceived image information in the mass storage device 1986. TheQuantification, Weighting, and Assessment Engine 1991 can then accessthe mass storage device 1986 as needed to. In some embodiments, theQuantification, Weighting, and Assessment Engine 1991 can accesscomputing systems 1994 and/or data sources 1995, or be accessed bycomputing systems 1985 and/or data sources 1995, through a web-enableduser access point. Connections can be a direct physical connection, avirtual connection, and other connection type. The web-enabled useraccess point can comprise a browser module that uses text, graphics,audio, video, and other media to present data and to allow interactionwith data via the network 1993.

The output module can be implemented as a combination of an all-pointsaddressable display such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. The output module can be implemented to communicate with inputdevices 1988 and they also include software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements, such as menus, windows, dialogue boxes, tool bars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the output module can communicate with a set ofinput and output devices to receive signals from the user.

Other Systems

The computing system 1985 can include one or more internal and/orexternal data sources (for example, data sources 1995). In someembodiments, one or more of the data repositories and the data sourcesdescribed above can be implemented using a relational database, such asDB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well asother types of databases such as a flat-file database, an entityrelationship database, and object-oriented database, and/or arecord-based database.

The computer system 1985 can also access one or more databases 1995. Thedata sources 1995 can be stored in a database or data repository. Thecomputer system 1985 can access the one or more data sources 1995through a network 1993 or can directly access the database or datarepository through I/O devices and interfaces 1988. The data repositorystoring the one or more data sources 1995 can reside within the computersystem 1985.

Additional Detail - General

In connection with any of the features and/or embodiments describedherein, in some embodiments, the system can be configured to analyze,characterize, track, and/or utilize one or more plaque features derivedfrom a medical image. For example, in some embodiments, the system canbe configured to analyze, characterize, track, and/or utilize one ormore dimensions of plaque and/or an area of plaque, in two dimensions,three dimensions, and/or four dimensions, for example over time orchanges over time. In addition, in some embodiments, the system can beconfigured to rank one or more areas of plaque and/or utilize suchranking for analysis. In some embodiments, the ranking can be binary,ordinal, continuous, and/or mathematically transformed. In someembodiments, the system can be configured to analyze, characterize,track, and/or utilize the burden or one or more geometries of plaqueand/or an area of plaque. For example, in some embodiments, the one ormore geometries can comprise spatial mapping in two dimensions, threedimensions, and/or four dimensions over time. As another example, insome embodiments, the system can be configured to analyze transformationof one or more geometries. In some embodiments, the system can beconfigured to analyze, characterize, track, and/or utilize diffusenessof plaque regions, such as spotty v. continuous. For example, in someembodiments, pixels or voxels within a region of interest can becompared to pixels or voxels outside of the region of interest to gainmore information. In particular, in some embodiments, the system can beconfigured to analyze a plaque pixel or voxel with another plaque pixelor voxel. In some embodiments, the system can be configured to compare aplaque pixel or voxel with a fat pixel or voxel. In some embodiments,the system can be configured to compare a plaque pixel or voxel with alumen pixel or voxel. In some embodiments, the system can be configuredto analyze, characterize, track, and/or utilize location of plaque orone or more areas of plaque. For example, in some embodiments, thelocation of plaque determined and/or analyzed by the system can includewhether the plaque is within the left anterior descending (LAD), leftcircumflex artery (LCx), and/or the right coronary artery (RCA). Inparticular, in some embodiments, plaque in the proximal LAD caninfluence plaque in the mid-LAD, and plaque in the LCx can influenceplaque in the LAD, such as via mixed effects modeling. As such, in someembodiments, the system can be configured to take into accountneighboring structures. In some embodiments, the location can be basedon whether it is in the proximal, mid, or distal portion of a vessel. Insome embodiments, the location can be based on whether a plaque is inthe main vessel or a branch vessel. In some embodiments, the locationcan be based on whether the plaque is myocardial facing or pericardialfacing (for example as an absolute binary dichotomization or as acontinuous characterization around 360 degrees of an artery), whetherthe plaque is juxtaposed to fat or epicardial fat or not juxtaposed tofat or epicardial fat, subtending a substantial amount of myocardium orsubtending small amounts of myocardium, and/or the like. For example,arteries and/or plaques that subtend large amounts of subtendedmyocardium can behave differently than those that do not. As such, insome embodiments, the system can be configured to take into account therelation to the percentage of subtended myocardium.

In connection with any of the features and/or embodiments describedherein, in some embodiments, the system can be configured to analyze,characterize, track, and/or utilize one or more peri-plaque featuresderived from a medical image. In particular, in some embodiments, thesystem can be configured to analyze lumen, for example in two dimensionsin terms of area, three dimensions in terms of volume, and/or fourdimensions across time. In some embodiments, the system can beconfigured to analyze the vessel wall, for example in two dimensions interms of area, three dimensions in terms of volume, and/or fourdimensions across time. In some embodiments, the system can beconfigured to analyze peri-coronary fat. In some embodiments, the systemcan be configured to analyze the relationship to myocardium, such as forexample a percentage of subtended myocardial mass.

In connection with any of the features and/or embodiments describedherein, in some embodiments, the system can be configured to analyzeand/or use medical images obtained using different image acquisitionprotocols and/or variables. In some embodiments, the system can beconfigured to characterize, track, analyze, and/or otherwise use suchimage acquisition protocols and/or variables in analyzing images. Forexample, image acquisition parameters can include one or more of mA,kVp, spectral CT, photon counting detector CT, and/or the like. Also, insome embodiments, the system can be configured to take into account ECGgating parameters, such as retrospective v. prospective ECG helical.Another example can be prospective axial v. no gating. In addition, insome embodiments, the system can be configured to take into accountwhether medication was used to obtain the image, such as for examplewith or without a beta blocker, with or without contrast, with orwithout nitroglycerin, and/or the like. Moreover, in some embodiments,the system can be configured to take into account the presence orabsence of a contrast agent used during the image acquisition process.For example, in some embodiments, the system can be configured tonormalize an image based on a contrast type, contrast-to-noise ratio,and/or the like. Further, in some embodiments, the system can beconfigured to take into account patient biometrics when analyzing amedical image. For example, in some embodiments, the system can beconfigured to normalize an image to Body Mass Index (BMI) of a subject,normalize an image to signal-to-noise ratio, normalize an image to imagenoise, normalize an image to tissue within the field of view, and/or thelike. In some embodiments, the system can be configured to take intoaccount the image type, such as for example CT, non-contrast CT, MRI,x-ray, nuclear medicine, ultrasound, and/or any other imaging modalitymentioned herein.

In connection with any of the features and/or embodiments describedherein, in some embodiments, the system can be configured to normalizeany analysis and/or results, whether or not based on image processing.For example, in some embodiments, the system can be configured tostandardize any reading or analysis of a subject, such as those derivedfrom a medical image of the subject, to a normative reference database.Similarly, in some embodiments, the system can be configured tostandardize any reading or analysis of a subject, such as those derivedfrom a medical image of the subject, to a diseased database, such as forexample patients who experienced heart attack, patients who areischemic, and/or the like. In some embodiments, the system can beconfigured to utilize a control database for comparison,standardization, and/or normalization purposes. For example, a controldatabase can comprise data derived from a combination of subjects, suchas 50% of subjects who experience heart attack and 50% who did not,and/or the like. In some embodiments, the system can be configured tonormalize any analysis, result, or data by applying a mathematicaltransform, such as a linear, logarithmic, exponential, and/or quadratictransform. In some embodiments, the system can be configured tonormalize any analysis, result, or data by applying a machine learningalgorithm.

In connection with any of the features and/or embodiments describedherein, in some embodiments, the term “density,” can refer toradiodensity, such as in Hounsfield units. In connection with any of thefeatures and/or embodiments described herein, in some embodiments, theterm “density,” can refer to absolute density, such as for example whenanalyzing images obtained from imaging modalities such as dual energy,spectral, photon counting CT, and/or the like. In some embodiments, oneor more images analyzed and/or accessed by the system can be normalizedto contrast-to-noise. In some embodiments, one or more images analyzedand/or accessed by the system can be normalized to signal-to-noise. Insome embodiments, one or more images analyzed and/or accessed by thesystem can be normalized across the length of a vessel, such as forexample along a transluminal attenuation gradient. In some embodiments,one or more images analyzed and/or accessed by the system can bemathematically transformed, for example by applying a logarithmic,exponential, and/or quadratic transformation. In some one or more imagesanalyzed and/or accessed by the system can be transformed using machinelearning.

In connection with any of the features and/or embodiments describedherein, in some embodiments, the term “artery” can include any artery,such as for example, coronary, carotid, cerebral, aortic, renal, lowerextremity, and/or upper extremity.

In connection with any of the features and/or embodiments describedherein, in some embodiments, the system can utilize additionalinformation obtained from various sources in analyzing and/or derivingdata from a medical image. For example, in some embodiments, the systemcan be configured to obtain additional information from patient historyand/or physical examination. In some embodiments, the system can beconfigured to obtain additional information from other biometric data,such as those which can be gleaned from wearable devices, which caninclude for example heart rate, heart rate variability, blood pressure,oxygen saturation, sleep quality, movement, physical activity, chestwall impedance, chest wall electrical activity, and/or the like. In someembodiments, the system can be configured to obtain additionalinformation from clinical data, such as for example from ElectronicMedical Records (EMR). In some embodiments, additional information usedby the system can be linked to serum biomarkers, such as for example ofcholesterol, renal function, inflammation, myocardial damage, and/or thelike. In some embodiments, additional information used by the system canbe linked to other omics markers, such as for example transcriptomics,proteomics, genomics, metabolomics, microbiomics, and/or the like.

In connection with any of the features and/or embodiments describedherein, in some embodiments, the system can utilize medical imageanalysis to derive and/or generate assessment of a patient and/orprovide assessment tools to guide patient assessment, thereby addingclinical importance and use. In some embodiments, the system can beconfigured to generate risk assessment at the plaque-level (for example,will this plaque cause heart attack and/or does this plaque causeischemia), vessel-level (for example, will this vessel be the site of afuture heart attack and/or does this vessel exhibit ischemia), and/orpatient level (for example, will this patient experience heart attackand/or the like). In some embodiments, the summation or weightedsummation of plaque features can contribute to segment-level features,which in turn can contribute to vessel-level features, which in turn cancontribute to patient-level features.

In some embodiments, the system can be configured to generate a riskassessment of future major adverse cardiovascular events, such as forexample heart attack, stroke, hospitalizations, unstable angina, stableangina, coronary revascularization, and/or the like. In someembodiments, the system can be configured to generate a risk assessmentof rapid plaque progression, medication non-response (for example ifplaque progresses significantly even when medications are given),benefit (or lack thereof) of coronary revascularization, new plaqueformation in a site that does not currently have any plaque, developmentof symptoms (such as angina, shortness of breath) that is attributableto the plaque, ischemia and/or the like. In some embodiments, the systemcan be configured to generate an assessment of other arteryconsequences, such as for example carotid (stroke), lower extremity(claudication, critical limb ischemia, amputation), aorta (dissection,aneurysm), renal artery (hypertension), cerebral artery (aneurysm,rupture), and/or the like.

Additional Detail - Determination of Non-Calcified Plaque From a MedicalImage(s)

As discussed herein, in some embodiments, the system can be configuredto determine non-calcified plaque from a medical image, such as anon-contrast CT image and/or image obtained using any other imagemodality as those mentioned herein. Also, as discussed herein, in someembodiments, the system can be configured to utilize radiodensity as aparameter or measure to distinguish and/or determine non-calcifiedplaque from a medical image. In some embodiments, the system can utilizeone or more other factors, which can be in addition to and/or used as analternative to radiodensity, to determine non-calcified plaque from amedical image.

For example, in some embodiments, the system can be configured toutilize absolute material densities via dual energy CT, spectral CT orphoton-counting detectors. In some embodiments, the system can beconfigured to analyze the geometry of the spatial maps that “look” likeplaque, for example compared to a known database of plaques. In someembodiments, the system can be configured to utilize smoothing and/ortransform functions to get rid of image noise and heterogeneity from amedical image to help determine non-calcified plaque. In someembodiments, the system can be configured to utilize auto-adjustableand/or manually adjustable thresholds of radiodensity values based uponimage characteristics, such as for example signal-to-noise ratios, bodymorph (for example obesity can introduce more image noise), and/or thelike. In some embodiments, the system can be configured to utilizedifferent thresholds based upon different arteries. In some embodiments,the system can be configured to account for potential artifacts, such asbeam hardening artifacts that may preferentially affect certain arteries(for example, the spine may affect right coronary artery in someinstances). In some embodiments, the system can be configured to accountfor different image acquisition parameters, such as for example,prospective vs. retrospective ECG gating, how much mA and kvP, and/orthe like. In some embodiments, the system can be configured to accountfor different scanner types, such as for example fast-pitch helical vs.traditional helical. In some embodiments, the system can be configuredto account for patient-specific parameters, such as for example heartrate, scan volume in imaged field of view, and/or the like. In someembodiments, the system can be configured to account for priorknowledge. For example, in some embodiments, if a patient had acontrast-enhanced CT angiogram in the past, the system can be configuredto leverage findings from the previous contrast-enhanced CT angiogramfor a non-contrast CT image(s) of the patient moving forward. In someembodiments, in cases where epicardial fat is not present outside anartery, the system can be configured to leverage other Hounsfield unitthreshold ranges to depict the outer artery wall. In some embodiments,the system can be configured to utilize a normalization device, such asthose described herein, to account for differences in scan results (suchas for example density values, etc.) between different scanners, scanparameters, and/or the like.

Additional Detail - Determination of Cause of Change in Calcium

As discussed herein, in some embodiments, the system can be configuredto determine a cause of change in calcium level of a subject byanalyzing one or more medical images. In some embodiments, the change incalcium level can be by some external force, such as for example,medication treatment, lifestyle change (such as improved diet, physicalactivity), stenting, surgical bypass, and/or the like. In someembodiments, the system is configured to include one or more assessmentsof treatment and/or recommendations of treatment based upon thesefindings.

In some embodiments, the system can be configured to determine a causeof change in calcium level of a subject and use the same for prognosis.In some embodiments, the system can be configured to enable improveddiagnosis of atherosclerosis, stenosis, ischemia, inflammation in theperi-coronary region, and/or the like. In some embodiments, the systemcan be configured to enable improved prognostication, such as forexample forecasting of some clinical event, such as major adversecardiovascular events, rapid progression, medication non-response, needfor revascularization, and/or the like. In some embodiments, the systemcan be configured to enable improved prediction, such as for exampleenabling identification of who will benefit from what therapy and/orenabling monitoring of those changes over time. In some embodiments, thesystem can be configured to enable improved clinical decision making,such as for example which medications may be helpful, which lifestyleinterventions might be helpful, which revascularization or surgicalprocedures may be helpful, and/or the like. In some embodiments, thesystem can be configured to enable comparison to one or more normativedatabases in order to standardize findings to a known ground truthdatabase.

In some embodiments, a change in calcium level can be linear,non-linear, and/or transformed. In some embodiments, a change in calciumlevel can be on its own or in other words involve just calcium. In someembodiments, a change in calcium level can be in relation to one or moreother constituents, such as for example, other non-calcified plaque,vessel volume/area, lumen volume/area, and/or the like. In someembodiments, a change in calcium level can be relative. For example, insome embodiments, the system can be configured to determine whether achange in calcium level is above or below an absolute threshold, whethera change in calcium level comprises a continuous change upwards ordownwards, whether a change in calcium level comprises a mathematicaltransform upwards or downwards, and/or the like.

As discussed herein, in some embodiments, the system can be configuredto analyze one or more variables or parameters, such as those relatingto plaque, in determining the cause of a change in calcium level. Forexample, in some embodiments, the system can be configured to analyzeone or more plaque parameters, such as a ratio or function of volume orsurface area, heterogeneity index, geometry, location, directionality,and/or radiodensity of one or more regions of plaque within the coronaryregion of the subject at a given point in time.

As discussed herein, in some embodiments, the system can be configuredto characterize a change in calcium level between two points in time.For example, in some embodiments, the system can be configured tocharacterize a change in calcium level as one of positive, neutral, ornegative. In some embodiments, the system can be configured tocharacterize a change in calcium level as positive when the ratio ofvolume to surface area of a plaque region has decreased, as this can beindicative of how homogeneous and compact the structure is. In someembodiments, the system can be configured to characterize a change incalcium level as positive when the size of a plaque region hasdecreased. In some embodiments, the system can be configured tocharacterize a change in calcium level as positive when the density of aplaque region has increased or when an image of the region of plaquecomprises more pixels with higher density values, as this can beindicative of stable plaque. In some embodiments, the system can beconfigured to characterize a change in calcium level as positive whenthere is a reduced diffuseness. For example, if three small regions ofplaque converge into one contiguous plaque, that can be indicative ofnon-calcified plaque calcifying along the entire plaque length.

In some embodiments, the system can be configured to characterize achange in calcium level as negative when the system determines that anew region of plaque has formed. In some embodiments, the system can beconfigured to characterize a change in calcium level as negative whenmore vessels with calcified plaque appear. In some embodiments, thesystem can be configured to characterize a change in calcium level asnegative when the ratio of volume to surface area has increased. In someembodiments, the system can be configured to characterize a change incalcium level as negative when there has been no increase in Hounsfielddensity per calcium pixel.

In some embodiments, the system can be configured to utilize anormalization device, such as those described herein, to account fordifferences in scan results (such as for example density values, etc.)between different scanners, scan parameters, and/or the like.

Additional Detail - Quantification of Plaque, Stenosis, And/or CAD-RADSScore

As discussed herein, in some embodiments, the system can be configuredto generate quantifications of plaque, stenosis, and/or CAD-RADS scoresfrom a medical image. In some embodiments, as part of suchquantification analysis, the system can be configured to determine apercentage of higher or lower density plaque within a plaque region. Forexample, in some embodiments, the system can be configured to classifyhigher density plaque as pixels or voxels that comprise a Hounsfielddensity unit above 800 and/or 1000. In some embodiments, the system canbe configured to classify lower density plaque as pixels or voxels thatcomprise a Hounsfield density unit below 800 and/or 1000. In someembodiments, the system can be configured to utilize other thresholds.In some embodiments, the system can be configured to report measures ona continuous scale, an ordinal scale, and/or a mathematicallytransformed scale.

In some embodiments, the system can be configured to utilize anormalization device, such as those described herein, to account fordifferences in scan results (such as for example density values, etc.)between different scanners, scan parameters, and/or the like.

Additional Detail - Disease Tracking

As discussed herein, in some embodiments, the system can be configuredto track the progression and/or regression of an arterial and/orplaque-based disease, such as atherosclerosis, stenosis, ischemia,and/or the like. For example, in some embodiments, the system can beconfigured to track the progression and/or regression of a disease overtime by analyzing one or more medical images obtained from two differentpoints in time. As an example, in some embodiments, one or more normalregions from an earlier scan can turn into abnormal regions in thesecond scan or vice versa.

In some embodiments, the one or more medical images obtained from twodifferent points in time can be obtained from the same modality and/ordifferent modalities. For example, scans from both points in time can beCT, whereas in some cases the earlier scan can be CT while the laterscan can be ultrasound.

Further, in some embodiments, the system can be configured to track theprogression and/or regression of disease by identifying and/or trackinga change in density of one or more pixels and/or voxels, such as forexample Hounsfield density and/or absolute density. In some embodiments,the system can be configured to track change in density of one or morepixels or voxels on a continuous basis and/or dichotomous basis. Forexample, in some embodiments, the system can be configured to classifyan increase in density as stabilization of a plaque region and/orclassify a decrease in density as destabilization of a plaque region. Insome embodiments, the system can be configured to analyze surface areaand/or volume of a region of plaque, ratio between the two, absolutevalues of surface area and/or volume, gradient(s) of surface area and/orvolume, mathematical transformation of surface area and/or volume,directionality of a region of plaque, and/or the like.

In some embodiments, the system can be configured to track theprogression and/or regression of disease by analyzing vascularmorphology. For example, in some embodiments, the system can beconfigured to analyze and/or track the effects of the plaque on theouter vessel wall getting bigger or smaller, the effects of the plaqueon the inner vessel lumen getting smaller or bigger, and/or the like.

In some embodiments, the system can be configured to utilize anormalization device, such as those described herein, to account fordifferences in scan results (such as for example density values, etc.)between different scanners, scan parameters, and/or the like.

Global Ischemia Index

Some embodiments of the systems, devices, and methods described hereinare configured to determine a global ischemia index that isrepresentative of risk of ischemia for a particular subject. Forexample, in some embodiments, the system is configured to generate aglobal ischemia index for a subject based at least in part on analysisof one or more medical images and/or contributors of ischemia as well asconsequences and/or associated factors to ischemia along the temporalischemic cascade. In some embodiments, the generated global ischemiaindex can be used by the systems, methods, and devices described hereinfor determining and/or predicting the outcome of one or more treatmentsand/or generating or guiding a recommended medical treatment, therapy,medication, and/or procedure for the subject.

In particular, in some embodiments, the systems, devices, and methodsdescribed herein can be configured to automatically and/or dynamicallyanalyze one or more medical images and/or other data to identify one ormore features, such as plaque, fat, and/or the like, for example usingone or more machine learning, artificial intelligence (AI), and/orregression techniques. In some embodiments, one or more featuresidentified from medical image data can be inputted into an algorithm,such as a second-tier algorithm which can be a regression algorithm ormultivariable regression equation, for automatically and/or dynamicallygenerating a global ischemia index. In some embodiments, the AIalgorithm for determining a global ischemia index can be configured toutilize one or more variables as input, such as different temporalstages of the ischemia cascade as described herein, and compare the sameto an output, such as myocardial blood flow, as a ground truth. In someembodiments, the output, such as myocardial blood flow, can beindicative of the presence or absence of ischemia as a binary measureand/or one or more moderations of ischemia, such as none, mild,moderate, severe, and/or the like.

In some embodiments, the system can be configured to utilize anormalization device, such as those described herein, to account fordifferences in scan results (such as for example density values, etc.)between different scanners, scan parameters, and/or the like.

In some embodiments, by utilizing one or more computer-implementedalgorithms, such as for example one or more machine learning and/orregression techniques, the systems, devices, and methods describedherein can be configured to analyze one or more medical images and/orother data to generate a global ischemia index and/or a recommendedtreatment or therapy within a clinical reasonable time, such as forexample within about 1 minute, about 2 minutes, about 3 minutes, about 4minutes, about 5 minutes, about 10 minutes, about 20 minutes, about 30minutes, about 40 minutes, about 50 minutes, about 1 hour, about 2hours, about 3 hours, and/or within a time period defined by two of theaforementioned values.

In generating the global ischemia index, in some embodiments, thesystems, devices, and methods described herein are configured to: (a)temporally integrate one or more variables along the “ischemic” pathwayand weight their input differently based upon their temporal sequence inthe development and worsening of coronary ischemia; and/or (b) integratethe contributors, associated factors and consequences of ischemia toimprove diagnosis of ischemia. Furthermore, in some embodiments, thesystems, devices, and methods described herein transcend analysis beyondjust the coronary arteries or just the left ventricular myocardium, andinstead can include a combination one or more of: coronary arteries;coronary arteries after nitroglycerin or vasodilator administration;relating coronary arteries to the fractional myocardial mass;non-cardiac cardiac examination; relationship of thecoronary-to-non-coronary cardiac; and/or non-cardiac examinations. Inaddition, in some embodiments, the systems, devices, and methodsdescribed herein can be configured to determine the fraction ofmyocardial mass or subtended myocardial mass to vessel or lumen volume,for example in combination with any of the other features describedherein such as the global ischemia index, to further determine and/orguide a recommended medical treatment or procedure, such asrevascularization, stenting, surgery, medication such as statins, and/orthe like. As such, in some embodiments, the systems, devices, andmethods described herein are configured to evaluate ischemia and/orprovide recommended medical treatment for the same in a manner that doesnot currently exist today, accounting for the totality of informationcontributing to ischemia.

In some embodiments, the system can be configured to differentiatebetween micro and macro vascular ischemia, for example based on analysisof one or more of epicardial coronaries, measures of myocardiumdensities, myocardium mass, volume of epicardial coronaries, and/or thelike. In some embodiments, by differentiating between micro and macrovascular ischemia, the system can be configured to generate differentprognostic and/or therapeutic approaches based on such differentiation.

In some embodiments, when a medical image(s) of a patient is obtained,such as for example using CT, MRI, and/or any other modality, not onlyinformation relating to coronary arteries but other information is alsoobtained, which can include information relating to the vascular systemand/or the rest of the heart and/or chest area that is within the frameof reference. While certain technologies may simply focus on theinformation relating to coronary arteries from such medical scans, someembodiments described herein are configured to leverage more of theinformation that is inherently obtained from such images to obtain amore global indication of ischemia and/or use the same to generateand/or guide medical therapy.

In particular, in some embodiments, the systems, devices, and methodsdescribed herein are configured to examine both the contributors as wellas consequences and associated factors to ischemia, rather than focusingonly on either contributors or consequences. In addition, in someembodiments, the systems, devices, and methods described herein areconfigured to consider the entirety and/or a portion of temporalsequence of ischemia or the “ischemic pathway.” Moreover, in someembodiments, the systems, devices, and methods described herein areconfigured to consider the non-coronary cardiac consequences as well asthe non-cardiac associated factors that contribute to ischemia. Further,in some embodiments, the systems, devices, and methods described hereinare configured to consider the comparison of pre- and post- coronaryvasodilation. Furthermore, in some embodiments, the systems, devices,and methods described herein are configured to consider a specific listof variables, rather than a general theme, appropriately weighting theircontribution to ischemia. Also, in some embodiments, the systems,devices, and methods described herein can be validated against multiple“measurements” of ischemia, including absolutely myocardial blood flow,myocardial perfusion, and/or flow ratios.

Generally speaking, ischemia diagnosis is currently evaluated by eitherstress tests (myocardial ischemia) or flow ratios in the coronary artery(coronary ischemia), the latter of which can include fractional flowreserve, instantaneous wave-free pressure ratio, hyperemic resistance,coronary flow, and/or the like. However, coronary ischemia can bethought of as only an indirect measure of what is going on in themyocardium, and myocardial ischemia can be thought of as only anindirect measure of what is going on in the coronary arteries.

Further certain tests measure only individual components of ischemia,such as contributors of ischemia (such as, stenosis) or sequelae ofischemia (such as, reduced myocardial perfusion or blood flow). However,there are numerous other contributors to ischemia beyond stenosis,numerous associated factors that increase likelihood of ischemia, andmany other early and late consequences of ischemia.

One technical shortcoming of such existing techniques is that if youonly look at factors that contribute or are associated with ischemia,then you are always too early— i.e., in the pre-ischemia stage.Conversely, if you only look at factors that are consequences/sequelaeof ischemia, then you are always too late—i.e., in the post-ischemiastage.

And ultimately, if you do not look at everything (including associativefactors, contributors, early and late consequences), you will notunderstand where an individual exists on the continuum of coronaryischemia. This may have very important implications in the type oftherapy an individual should undergo - such as for example medicaltherapy, intensification of medical therapy, coronary revascularizationby stenting, and/or coronary revascularization by coronary artery bypasssurgery. As such, in some embodiments described herein, the systems,methods, and devices are configured to generate or determine a globalischemia index for a particular patient based at least in part onanalysis of one or more medical images or data of the patient, whereinthe generated global ischemia index is a measure of ischemia for thepatient along the continuum of coronary ischemia or the ischemic cascadeas described in further detail below. In other words, in someembodiments, unlike in existing technologies or techniques, the globalischemia index generated by the system can be indicative of a stage orrisk or development of ischemia of a particular patient along thecontinuum of coronary ischemia or the ischemic cascade.

Further, there can be a relationship between the things thatcontribute/cause ischemia and the consequences/sequelae of ischemia thatoccur in a continuous and overlapping fashion. Thus, it can be much moreaccurate to identify ischemic individuals by combining various factorsthat contribute/cause ischemia with factors that areconsequences/sequelae of ischemia.

As such, in some embodiments described herein, the systems, devices, andmethods are configured to analyze one or more associative factors,contributors, as well as early and late consequences of ischemia ingenerating a global ischemia index, which can provide a more globalindication of the risk of ischemia. Further, in some embodimentsdescribed herein, the systems, devices, and methods are configured touse such generated global ischemia index to determine and/or guide atype of therapy an individual should undergo, such as for examplemedical therapy, intensification of medical therapy, coronaryrevascularization by stenting, and/or coronary revascularization bycoronary artery bypass surgery.

As discussed herein, in some embodiments, the systems, devices, andmethods are configured to generate a global ischemia index indicativeand/or representative of a risk of ischemia for a particular subjectbased on one or more medical images and/or other data. Morespecifically, in some embodiments, the system can be configured togenerate a global ischemia index as a measurement of myocardialischemia.

In some embodiments, the generated global ischemia index provides a muchmore accurate and/or direct measurement of myocardial ischemia comparedto existing techniques. Ischemia, by its definition, is an inadequateblood supply to an organ or part of the body. By this definition, thediagnosis of ischemia can be best performed by examining therelationship of the coronary arteries (blood supply) to the heart (organor part of the body). However, this is not the case as currentgeneration tests measure either the coronary arteries (e.g., FFR, iFR)or the heart (e.g. stress testing by nuclear SPECT, PET, CMR or echo).Because current generation tests fail to examine the relationships ofthe coronary arteries, they do not account for the temporal sequence ofevents that occurs in the evolution of ischemia (from none-to-some, aswell as from mild-to-moderate-to-severe) or the “ischemic pathway,” aswill be described in more detail herein. Quantifying the relationship ofthe coronary arteries to the heart and other non-coronary structures tothe manifestation of ischemia, as well as the temporal findingsassociated with the stages of ischemia in the ischemic cascade, canimprove our accuracy of diagnosis—as well as our understanding ofischemia severity—in a manner not possible with current generationtests.

As discussed above, no test currently exists for directly measuringischemia; rather, existing tests only measure certain specific factorsor surrogate markers associated with ischemia, such as for examplehypoperfusion or fractional flow reserve (FFR) or wall motionabnormalities. In other words, the current approaches to ischemiaevaluation are entirely too simplistic and do not consider all of thevariables.

Ischemia has historically been “measured” by stress tests. The possiblestress tests that exist include: (a) exercise treadmill ECG testingwithout imaging; (b) stress testing by single photon emission computedtomography (SPECT); (c) stress testing by positron emission tomography(PET); (d) stress testing by computed tomography perfusion (CTP); (e)stress testing by cardiac magnetic resonance (CMR) perfusion; and (f)stress testing by echocardiography. Also, SPECT, PET, CTP and CMR canmeasure relative myocardial perfusion, in that you compare the mostnormal appearing portion of the left ventricular myocardium to theabnormal-appearing areas. PET and CTP can have the added capability ofmeasuring absolute myocardial blood flow and using these quantitativemeasures to assess the normality of blood supply to the left ventricle.In contrast, exercise treadmill ECG testing measures ST-segmentdepression as an indirect measure of subendocardial ischemia (reducedblood supply to the inner portion of the heart muscle), while stressechocardiography evaluates the heart for stress-induced regional wallmotion abnormalities of the left ventricle. Abnormal relative perfusion,absolute myocardial blood flow, ST segment depression and regional wallmotion abnormalities occur at different points in the “ischemicpathway.”

Furthermore, in contrast to myocardial measures of the left ventricle,alternative methods to determine ischemia involve direct evaluation ofthe coronary arteries with pressure or flow wires. The most common 2measurements are fractional flow reserve (FFR) or iFR. These techniquescan compare the pressure distal to a given coronary stenosis to thepressure proximal to the stenosis. While easy to understand andpotentially intuitive, these techniques do not account for importantparameters that can contribute to ischemia, including diffuseness of“mild” stenoses, types of atherosclerosis causing stenosis; and thesetechniques take into account neither the left ventricle in whole nor the% left ventricle subtended by a given artery.

In some embodiments, the global ischemia index is a measure ofmyocardial ischemia, and leverages the quantitative informationregarding the contributors, associated factors and consequences ofischemia. Further, in some embodiments, the system uses these factors toaugment ischemia prediction by weighting their contribution accordingly.In some embodiments, the global ischemia index is aimed to serve as adirect measure of both myocardial perfusion and coronary pressure and tointegrate these findings to improve ischemia diagnosis.

In some embodiments, unlike existing ischemia “measurement” techniquesthat focus only on a single factor or a single point in the ischemicpathway, the systems, devices, and methods described herein areconfigured to analyze and/or use as inputs one or more factors occurringat different points in the ischemic pathway in generating the globalischemia index. In other words, in some embodiments, the systems,devices, and methods described herein are configured to take intoaccount the whole temporal ischemic cascade in generating a globalischemia index for assessing the risk of ischemia and/or generating arecommended treatment or therapy for a particular subject.

FIG. 20A illustrates one or more features of an example ischemicpathway. While the ischemic pathway is not definitively proven, it isthought to be as shown in FIG. 20A. Having said this, this ischemicpathway may not actually occur in this exact sequence. The ischemicpathway may in fact occur in different order, or many of the events mayoccur simultaneously and overlap. Nonetheless, the different pointsalong the ischemic pathway can occur at different points in time,thereby adding a temporal aspect in the development of ischemia thatsome embodiments described herein consider.

As illustrated in FIG. 20A, the ischemic pathway can illustratedifferent conditions that can occur when you have a blockage in a heartartery that reduces blood supply to the heart muscle. In other words,the ischemic pathway can illustrate a sequence of pathophysiologicevents caused by coronary artery disease. As illustrated in FIG. 20A,ischemia can occur or gradually develop in a number of different stepsrather than a binary concept. The ischemic pathway illustrates differentconditions that may arise as a patient gets more and more ischemic.

Different existing tests can show ischemia at different stages along theischemic pathway. For example, a nuclear stress test can show ischemiasooner rather than an echo test, because nuclear imaging probeshypoperfusion, which is an earlier event in the ischemic pathway,whereas a stress echocardiography probes a later event such as systolicdysfunction. Further, an exercise treadmill EKG testing can showischemia sometime after an echo stress test, as if EKG testing becomesabnormal ECG changes will show. In addition, a PET scan can measure flowmaldistribution, and as such can show signs of ischemia prior to beforenuclear stress tests. As such, different tests exist for measuringdifferent conditions and steps along the ischemic cascade. However,there does not exist a global technique that takes into account all ofthese different conditions that arise throughout the course of theischemic pathway. As such, in some embodiments herein, the systems,devices and methods are configured to analyze multiple differentmeasures along the temporal ischemic pathway and/or weight themdifferently in generating a global ischemia index, which can be used todiagnose ischemia and/or provide a recommended therapy and/or treatment.In some embodiments, such multiple measures along the temporal ischemicpathway can be weighted differently in generating the global ischemicindex; for example, certain measures that come earlier can be weightedless than those measures that arise later in the ischemic cascade insome embodiments. More specifically, in some embodiments, one or moremeasures of ischemia can be weighted from less to more heavily in thefollowing general order: flow maldistribution, hypoperfusion, diastolicdysfunction, systolic dysfunction, ECG changes, angina, and/or regionalwall motion abnormality.

In some embodiments, the system can be configured to take the temporalsequence of the ischemic pathway and integrate and weight variousconditions or events accordingly in generating the global ischemiaindex. Further, in some embodiments, the system can be configured toidentify certain conditions or “associative factors” well before actualsigns ischemia occur, such as for example fatty liver which isassociated with diabetes which is associated with coronary disease. Inother words, in some embodiments, the system can be configured tointegrate one or more factors that are associated, causal, contributive,and/or consequential to ischemia, take into account the temporalsequence of the same and weight them accordingly to generate an indexrepresentative of and/or predicting risk of ischemia and/or generating arecommend treatment.

As discussed herein, the global ischemia index generated by someembodiments provide substantial technical advantages over existingtechniques for assessing ischemia, which have a number of shortcomings.For example, coronary artery examination alone does not consider thewealth of potential contributors to ischemia, including for example: (1)3D flow (lumen, stenosis, etc.); (2) endothelial function / vasodilation/ vasoconstrictive ability of the coronary artery (e.g., plaque type,burden, etc.); (3) inflammation that may influence the vasodilation /vasoconstrictive ability of the coronary artery (e.g., epicardialadipose tissue surrounding the heart); and/or (4) location (plaques thatface the myocardium are further away from the epicardial fat, and may beless influenced by the inflammatory contribution of the fat. Plaquesthat are at the bifurcation, trifurcation or proximal/ostial locationmay influence the likelihood of ischemia more than those that are not atthe bifurcation, trifurcation or proximal/ostial location).

One important consideration is that current methods for determiningischemia by CT rely primarily on computational fluid dynamics which, byits definition, does not include fluid-structure interactions (FSI).However, the use of FSI requires the understanding of the materialdensities of coronary artery vessels and their plaque constituents,which is not known well.

Thus, in some embodiments described herein, one important component isthat the lateral boundary conditions in the coronary arteries (lumenwall, vessel wall, plaque) can be known in a relative fashion by settingHounsfield unit thresholds that represent different material densitiesor setting absolute material densities to pixels based upon comparisonto a known material density (i.e., normalization device in our priorpatent). By doing so, and coupling to a machine learning algorithm, someembodiments herein can improve upon the understanding of fluid-structureinteractions without having to understand the exact material density,which may inform not only ischemia (blood flow within the vessel) butthe ability of a plaque to “fatigue” over time.

In addition, in some embodiments, the system is configured to take intoaccount non-coronary cardiac examination and data in addition tocoronary cardiac data. The coronary arteries supply blood to not onlythe left ventricle but also the other chambers of the heart, includingthe left atrium, the right ventricle and the right atrium. Whileperfusion is not well measured in these chambers by current generationstress tests, in some embodiments, the end-organ effects of ischemia canbe measured in these chambers by determining increases in blood volumeor pressure (i.e., size or volumes). Further, if blood volume orpressure increases in these chambers, they can have effects of “backingup” blood flow due to volume overload into the adjacent chambers orvessels. So, as a chain reaction, increases in left ventricular volumemay increase volumes in sequential order of: (1) left atrium; (2)pulmonary vein; (3) pulmonary arteries; (4) right ventricle; (5) rightatrium; (6) superior vena cava or inferior vena cava. In someembodiments, by taking into account non-coronary cardiac examination,the system can be configured to differentiate the role of ischemia onthe heart chambers based upon how “upstream” or “downstream” they are inthe ischemic pathway.

Moreover, in some embodiments, the system can be configured to take intoaccount the relationship of coronary arteries and non-coronary cardiacexamination. Existing methods of ischemia determination limit theirexamination to either the coronary arteries (e.g., FFR, iFR) or theheart left ventricular myocardium. However, in some embodiments herein,the relationship of the coronary arteries with the heart chambers mayact synergistically to improve our diagnosis of ischemia.

Further, in some embodiments, the system can be configured to take intoaccount non-cardiac examination. At present, no method of coronary /myocardial ischemia determination accounts for the effects of clinicalcontributors (e.g., hypertension, diabetes) on the likelihood ofischemia. However, these clinical contributors can manifest severalimage-based end-organ effects which may increase the likelihood of anindividual to manifest ischemia. These can include such image-basedsigns such as aortic dimension (aneurysms are a common end-organ effectof hypertension) and/or non-alcoholic steatohepatitis (fatty liver is acommon end-organ effect of diabetes or pre-diabetes). As such, in someembodiments, the system can be configured to account for these featuresto augment the likelihood of ischemia diagnosis on a scan-specific,individualized manner.

Furthermore, at present, no method of myocardial ischemia determinationincorporates other imaging findings that may not be ascertainable by asingle method, but can be determined through examination by othermethods. For example, the ischemia pathway is often thought to occur, insequential order, from metabolic alterations (laboratory tests),perfusion abnormalities (stress perfusion), diastolic dysfunction(echocardiogram), systolic dysfunction (echocardiogram or stress test),ECG changes (ECG) and then angina (chest pain, human patient report). Insome embodiments, the system can be configured to integrate thesefactors with the image-based findings of the CT scan and allow forimprovements in ischemia determination by weighting these variables inaccordance with their stage of the ischemic cascade.

As described herein, in some embodiments, the systems, methods, anddevices are configured to generate a global ischemia index to diagnoseischemia. In some embodiments, the global ischemia index considers thetotality of findings that contribute to ischemia, including, for exampleone or more of: coronary arteries + nitroglycerin / vasodilatoradministration + relating coronary arteries to the fractional myocardialmass + non-cardiac cardiac examination + relationship of thecoronary-to-non-coronary cardiac + non-cardiac examinations, and/or asubset thereof. In some embodiments, the global ischemia index providesweighted increases of variables to contribution of ischemia based uponwhere the image-based finding is in the pathophysiology of ischemia. Insome embodiments, in generating the global ischemia index, the system isconfigured to input into a regression model one or more factors that areassociative, contributive, casual, and/or consequential to ischemia tooptimally diagnose whether a subject ischemic or not.

FIG. 20B is a block diagram depicting one or more contributors and oneor more temporal sequences of consequences of ischemia utilized by anexample embodiment(s) described herein. As illustrated in FIG. 20B, insome embodiments, the system can be configured to analyze a number offactors, including contributors, associated factors, causal factors,and/or consequential factors of ischemia and/or use the same as inputfor generating the global ischemia index. Some of such factors caninclude those conditions shown in FIG. 20B. For example, signs of afatty liver and/or emphysema in the lungs can be associated factors usedby the system as inputs for generating the global ischemia index. Someexamples of contributors used as an input(s) by the system can includethe inability to vasodilate with nitric oxide and/or nitroglycerin, lowdensity non-calcified plaque, small artery, and/or the like. Someexamples of early consequences of ischemia used as an input(s) by thesystem can include reduced perfusion in the heart muscle, increase insize of the volume of the heart. An example of late consequences ofischemia used as an input(s) by the system can include blood starting toback up into other chambers of heart in addition to the left ventricle.

In some embodiments, the global ischemia index accounts for the directcontributors to ischemia, the early consequences of ischemia, the lateconsequences of ischemia, the associated factors with ischemia and othertest findings in relation to ischemia. In some embodiments, one or morethese factors can be identified and/or derived automatically,semi-automatically, and/or dynamically using one or more algorithms,such as a machine learning algorithm. Some example algorithms foridentifying such features are described in more detail below. Withoutsuch trained algorithms, it can be difficult, if not impossible, to takeinto account all of these factors in generating the global ischemiaindex within a reasonable time.

In some embodiments, these factors, weighted differently andappropriately, can improve diagnosis of ischemia. FIG. 20C is a blockdiagram depicting one or more features of an example embodiment(s) fordetermining ischemia by weighting different factors differently. In someembodiments, in generating the global ischemia index, the system isconfigured to take into account the temporal aspect of the ischemiccascade and weight one or more factors according to the temporal aspect,for example where early signs of ischemia can be weighted less heavilycompared to later signs of ischemia. In some embodiments, the system canautomatically and/or dynamically determine the different weights foreach factor, for example using a regression model. In some embodiments,the system can be configured to derive one or more appropriate weightingfactors based on previous analysis of data to determine which factorshould be more or less heavily weighted compared to others. In someembodiments, a user can guide and/or otherwise provide input forweighting different factors.

As described herein, in some embodiments, the global ischemia index canbe generated by a machine learning algorithm and/or a regressionalgorithm that condenses this multidimensional information into anoutput of “ischemia” or “no ischemia” when compared to a “gold standard”of ischemia, as measured by myocardial blood flow, myocardial perfusionor flow ratios. In some embodiments, the system can be configured tooutput an indication of moderation of ischemia, such none, mild,moderate, severe, and/or the like. In some embodiments, the outputindication of ischemia can be on a continuous scale.

FIG. 20D is a block diagram depicting one or more features of an exampleembodiment(s) for calculating a global ischemia index. As illustrated inFIG. 20D, in some embodiments, the system can be configured to validatethe outputted global ischemia index against absolute myocardial bloodflow, which can be measured for example by PET and/or CT scans tomeasure different regions of the heart to see if there are differentflows of blood within different regions. As absolute myocardial bloodflow can provide an absolute value of volume per time, in someembodiments, the system can be configured to compare the absolutemyocardial blood flow of one region to another region, which would notbe possible using relative measurements, such as for example usingnuclear stress testing.

As discussed herein, in some embodiments, the systems, devices, andmethods can be configured to utilize a machine learning algorithm and/orregression algorithm for analyzing and/or weighting different factorsfor generating the global ischemia index. By doing so, in someembodiments, the system can be configured to take into account one ormore statistical and/or machine learning considerations. Morespecifically, in some embodiments, the system can be configured todeliberately duplicate the contribution of particular variables. Forexample, in some embodiments, non-calcified plaque (NCP), low densitynon-calcified plaque (LD-NCP), and/or high-risk plaque (HRP) may allcontribute to ischemia. In traditional statistics, collinearity could bea reason to select only one out of these three variables, but byutilizing machine learning in some embodiments, the system may allow fordata driven exploration of the contribution of multiple variables, evenif they share a specific feature. In addition, in some embodiments, thesystem may take into account certain temporal considerations whentraining and/or applying an algorithm for generating the global ischemiaindex. For example, in some embodiments, the system can be configured togive greater weight to consequences/sequelae rather thancauses/contributors, as the consequences/sequelae have already occurred.

In addition, in some situations, coronary vasodilation is induced beforea coronary CT scan because it allows the coronary arteries to be maximumin their size/volume. Nitroglycerin is an endothelium-independentvasodilator as compared to, for example, nitric oxide, which is anendothelium-dependent vasodilator. As nitroglycerin-induced vasodilationoccurs in the coronary arteries—and, because a “timing” iodine contrastbolus is often administered before the actual coronary CT angiogram,comparison of the volume of coronary arteries before and after anitroglycerin administration may allow a direct evaluation of coronaryvasodilatory capability, which may significantly augment accurateischemia diagnosis. Alternatively, an endothelium-dependentvasodilator-like nitric oxide or carbon dioxide—may allow foraugmentation of coronary artery size in a manner that can be eitherreplaced or coupled to endothelium-independent vasodilation (bynitroglycerin) to maximize understanding of the ability of coronaryarteries to vasodilate.

In some embodiments, the system can be configured to measurevasodilatory effects, for example by measuring the diameter of one ormore arteries before and/or after administration of nitroglycerin and/ornitric oxide, and use such vasodilatory effects as a direct measurementor indication of ischemia. Alternatively and/or in addition to theforegoing, in some embodiments, the system can be configured to measuresuch vasodilatory effects and use the same as an input in determining orgenerating the global ischemia index and/or developing a recommendedmedical therapy or treatment for the subject.

Further, in some embodiments, the system can be configured to relate thecoronary arteries to the heart muscle that it provides blood to. Inother words, in some embodiments, the system can be configured to takeinto account fractional myocardial mass when generating a globalischemia index. For ischemia diagnosis, stress testing can be, atpresent, limited to the left ventricle. For example, in stressechocardiogram (ultrasound), the effects of stress-induced leftventricular regional wall motion abnormalities are examined, while inSPECT, PET and cardiac MRI, the effects of stress-induced leftventricular myocardial perfusion are examined. However, no currentlyexisting technique relates the size (volume), geometry, path andrelation to other vessels with the % fractional myocardial masssubtended by that artery. Further, one assumes that the coronary arterydistribution is optimal but, in many people, it may not be. Therefore,understanding an optimization platform to compute optimal blood flowthrough the coronary arteries may be useful in guiding treatmentdecisions.

As such, in some embodiments, the system is configured to determine thefractional myocardial mass or the relationship of coronary arteries tothe left ventricular myocardium that they subtend. In particular, insome embodiments, the system is configured to determine and/or tack intoaccount the subtended mass of myocardium to the volume of arterialvessel. Historically, myocardial perfusion evaluation for myocardialischemia has been performed using stress tests, such as nuclear SPECT,PET, cardiac MRI or cardiac CT perfusion. These methods have relied upona 17-segment myocardial model, which classifies perfusion defects bylocation. There can be several limitations to this, including: (1)assuming that all 17 segments have the same size; (2) assuming that all17 segments have the same prognostic importance; and (3) does not relatethe myocardial segments to the coronary arteries that provide bloodsupply to them.

As such, to address such shortcomings, in some embodiments, the systemcan be configured to analyze fractional myocardial mass (FMM). Generallyspeaking, FMM aims to relate the coronary arteries to the amount ofmyocardium that they subtend. These can have important implications onprognostication and treatment. For example, a patient may have a 70%stenosis in an artery, which has been a historical cut point wherecoronary revascularization (stenting) is considered. However, there maybe very important prognostic and therapeutic implications for patientswho have a 70% stenosis in an artery that subtends 1% of the myocardiumvs. a 70% stenosis in an artery that subtends 15% of the myocardium.

This FMM has been historically calculated using a “stem-and-crown”relationship between the myocardium on CT scans and the coronaryarteries on CT scans and has been reported to have the followingrelationship: M = kL¾, where M = mass, k = constant, and L = length.

However, this relationship, while written about quite frequently, hasnot been validated extensively. Nor have there been any cut points thatcan effectively guide therapy. The guidance of therapy can come in manyregards, including: (1) decision to perform revascularization: high FMM,perform revascularization to improve event-free survival; low FMM,medical therapy alone without revascularization; (2) different medicaltherapy regimens: high FMM, give several medications to improveevent-free survival; low FMM, give few medications; (3) prognostication:high FMM, poor prognosis; low FMM, good prognosis.

Further, in the era of 3D imaging, the M=kL relationships should beexpanded to the M=kV relationship, where V = volume of the vessel orvolume of the lumen. As such, in some embodiments, the system isconfigured to (1) describe the allometric scaling law in 3 dimensions,i.e., M=kVn; (2) use FMM as a cut point to guide coronaryrevascularization; and/or (3) use FMM cut points for clinical decisionmaking, including (a) use of medications vs. not, (b) different types ofmedications (cholesterol lowering, vasodilators, heart rate slowingmedications, etc.) based upon FMM cut points; (c) number of medicationsbased upon FMM cut points; and/or (d) prognostication based upon FMM cutpoints. In some embodiments, the use of FMM cut points by 3D FMMcalculations can improve decision making in a manner that improvesevent-free survival.

As described above, in some embodiments, the system can be configured toutilize one or more contributors or causes of ischemia as inputs forgenerating a global ischemia index. An example of a contributor or causeof ischemia that can be utilized as input and/or analyzed by the systemcan include vessel caliber. In particular, in some embodiments, thesystem can be configured to analyze and/or utilize as an input thepercentage diameter of stenosis, wherein the greater the stenosis themore likely the ischemia. In addition, in some embodiments, the systemcan be configured to analyze and/or utilize as in input lumen volume,wherein the smaller the lumen volume, the more likely the ischemia. Insome embodiments, the system can be configured to analyze and/or utilizeas an input lumen volume indexed to % fractional myocardial mass, bodysurface area (BSA), body mass index (BMI), left ventricle (LV) mass,overall heart size, wherein the smaller the lumen volume, the morelikely the ischemia. In some embodiments, the system can be configuredto analyze and/or utilize as an input vessel volume, wherein the smallerthe vessel volume, the more likely the ischemia. In some embodiments,the system can be configured to analyze and/or utilize as an inputminimal luminal diameter (MLD), minimal luminal are (MLA), and/or aratio between MLD and MLA, such as MLD/MLA.

Another example contributor or cause of ischemia that can be utilized asinput and/or analyzed by the system can include plaque, which may havemarked effects on the ability of an artery to vasodilate/ vasoconstrict.In particular, in some embodiments, the system can be configured toanalyze and/or utilize as an input non-calcified plaque (NCP), which maycause greater endothelial dysfunction and inability to vasodilate tohyperemia. In some embodiments, the system may utilize one or morearbitrary cutoffs for analyzing NCP, such as binary, trinary, and/or thelike for necrotic core, fibrous, and/or fibrofatty. In some embodiments,the system may utilize continuous density measures for NCP. Further, insome embodiments, the system may analyze NCP for dual energy,monochromatic, and/or material basis decomposition. In some embodiments,the system can be configured to analyze and/or identify plaque geometryand/or plaque heterogeneity and/or other radiomics features. In someembodiments, the system can be configured to analyze and/or identifyplaque facing the lumen and/or plaque facing epicardial fat. In someembodiments, the system can be configured to derive and/or identifyimaging-based information, which can be provided directly to thealgorithm for generating the global ischemia index.

In some embodiments, the system can be configured to analyze and/orutilize as an input low density NCP, which may cause greater endothelialdysfunction and inability to vasodilate to hyperemia, for example usingone or more specific techniques described above in relation to NCP. Insome embodiments, the system can be configured to analyze and/or utilizeas an input calcified plaque (CP), which may cause more laminar flow,less endothelial dysfunction and less ischemia. In some embodiments, thesystem may utilize one or more arbitrary cutoffs, such as 1K plaque(plaques >1000 Hounsfield units), and/or continuous density measures forCP.

In some embodiments, the system can be configured to analyze and/orutilize as an input the location of plaque. In particular, the systemmay determine that myocardial facing plaque may be associated withreduced ischemia due to its proximity to myocardium (e.g., myocardialbridging rarely has atherosclerosis). In some embodiments, the systemmay determine that pericardial facing plaque may be associated withincreased ischemia due to its proximity to peri-coronary adipose tissue.In some embodiments, the system may determine that bifurcation and/ortrifurcation lesions may be associated with increased ischemia due todisruptions in laminar flow.

In some embodiments, visualization of three-dimensional plaques can begenerated and/or provided by the system to a user to improveunderstanding to the human observer of where plaques are in relationshipto each other and/or to the myocardium to the pericardium. For example,in a particular vein, the system may be configured to allow thevisualization of all the plaques on a single 2D image. As such, in someembodiments, the system can allow for all of the plaques to bevisualized in a single view, with color-coded and/or shadowed labelsand/or other labels to plaques depending on whether they are in the 2Dfield of view, or whether they are further away from the 2D field ofview. This can be analogous to the maximum intensity projection view,which highlights the lumen that is filled with contrast agent, butapplies an intensity projection (maximum, minimum, average, ordinal) tothe plaques of different distance from the field of view or of differentdensities.

In some embodiments, the system can be configured to visualize plaqueusing maximum intensity projection (MIP) techniques. In someembodiments, the system can be configured to visualize plaque in 2D, 3D,and/or 4D, for example using MIP techniques and/or other techniques,such as volume rendering techniques (VRT). More specifically, for 4D, insome embodiments, the system can be configured to visualize progressionof plaque in terms of time. In some embodiments, the system can beconfigured to visualize on an image and/or on a video and/or otherdigital support the lumen and/or the addition of plaque in 2D, 3D,and/or 4D. In some embodiments, the system can be configured to showchanges in time or 4D. In some embodiments, the system can be configuredto take multiple scans taken from different points in time and/orintegrate all or some of the information with therapeutics. In someembodiments, based on the same, the system can be configured to decideon changes in therapy and/or determine prognostic information, forexample assessing for therapy success.

Another example contributor or cause of ischemia that can be utilized asinput and/or analyzed by the system can include fat. In someembodiments, the system can be configured to analyze and/or utilize asan input peri-coronary adipose tissue, which may cause ischemia due toinflammatory properties that cause endothelial dysfunction. In someembodiments, the system can be configured to analyze and/or utilize asan input epicardial adipose tissue, which may be a cause of overallheart inflammation. In some embodiments, the system can be configured toanalyze and/or utilize as input epicardial fat and/or radiomics orimaging-based information provided directly to the algorithm, such asfor example heterogeneity, density, density change away from the vessel,volume, and/or the like.

As described above, in some embodiments, the system can be configured toutilize one or more consequences or sequelae of ischemia as inputs forgenerating a global ischemia index. An example consequence or sequelaeof ischemia that can be utilized as input and/or analyzed by the systemcan be related to the left ventricle. For example, in some embodiments,the system can be configured to analyze the perfusion and/or Hounsfieldunit density of the left ventricle, which can be global and/or relatedto the percentage of fractional myocardial mass. In some embodiments,the system can be configured to analyze the mass of the left ventricle,wherein the greater the mass, the greater the potential mismatch betweenlumen volume to LV mass, which can be global as well as related to thepercentage of fractional myocardial mass. In some embodiments, thesystem can be the system can be configured to analyze the volume of theleft ventricle, wherein an increase in the left ventricle volume can bea direct sign of ischemia. In some embodiments, the system can beconfigured to analyze and/or utilize as input density measurements ofthe myocardium, which can be absolute and/or relative, for example usinga sticker or normalization device. In some embodiments, the system canbe configured to analyze and/or use as input regional and/or globalchanges in densities. In some embodiments, the system can be configuredto analyze and/or use as input endo, mid-wall, and/or epicardial changesin densities. In some embodiments, the system can be configured toanalyze and/or use as input thickness, presence of fat and/orlocalization thereof, presence of calcium, heterogeneity, radiomicfeatures, and/or the like.

Another example consequence or sequelae of ischemia that can be utilizedas input and/or analyzed by the system can be related to the rightventricle. For example, in some embodiments, the system can beconfigured to analyze the perfusion and/or Hounsfield unit density ofthe right ventricle, which can be global and/or related to thepercentage of fractional myocardial mass. In some embodiments, thesystem can be configured to analyze the mass of the right ventricle,wherein the greater the mass, the greater the potential mismatch betweenlumen volume to LV mass, which can be global as well as related to thepercentage of fractional myocardial mass. In some embodiments, thesystem can be the system can be configured to analyze the volume of theright ventricle, wherein an increase in the right ventricle volume canbe a direct sign of ischemia.

Another example consequence or sequelae of ischemia that can be utilizedas input and/or analyzed by the system can be related to the leftatrium. For example, in some embodiments, the system can be configuredto analyze the volume of the left atrium, in which an increased leftatrium volume can occur in patients who become ischemic and go intoheart failure.

Another example consequence or sequelae of ischemia that can be utilizedas input and/or analyzed by the system can be related to the rightatrium. For example, in some embodiments, the system can be configuredto analyze the volume of the right atrium, in which an increased rightatrium volume can occur in patients who become ischemic and go intoheart failure.

Another example consequence or sequelae of ischemia that can be utilizedas input and/or analyzed by the system can be related to one or moreaortic dimensions. For example, an increased aortic size as along-standing contributor of hypertension may be associated with theend-organ effects of hypertension on the coronary arteries (resulting inmore disease) and the LV mass (resulting in more LV mass-coronary lumenvolume mismatch).

Another example consequence or sequelae of ischemia that can be utilizedas input and/or analyzed by the system can be related to the pulmonaryveins. For example, for patients with volume overload, engorgement ofthe pulmonary veins may be a significant sign of ischemia.

As described above, in some embodiments, the system can be configured toutilize one or more associated factors of ischemia as inputs forgenerating a global ischemia index. An example associated factor ofischemia that can be utilized as input and/or analyzed by the system canbe related to the presence of fatty liver or non-alcoholicsteatohepatitis, which is a condition that can be diagnosed by placingregions of interest (ROIs) in the liver to measure Hounsfield unitdensities. Another example associated factor of ischemia that can beutilized as input and/or analyzed by the system can be related toemphysema, which is a condition that can be diagnosed by placing regionsof interest in the lung to measure Hounsfield unit densities. Anotherexample associated factor of ischemia that can be utilized as inputand/or analyzed by the system can be related to osteoporosis, which is acondition that can be diagnosed by placing regions of interest in thespine. Another example associated factor of ischemia that can beutilized as input and/or analyzed by the system can be related to mitralannular calcification, which is a condition that can be diagnosed byidentifying calcium (e.g., HU>350 etc.) in the mitral annulus. Anotherexample associated factor of ischemia that can be utilized as inputand/or analyzed by the system can be related to aortic valvecalcification, which is a condition that can be diagnosed by identifyingcalcium in the aortic valve. Another example associated factor ofischemia that can be utilized as input and/or analyzed by the system canbe related to aortic enlargement, often seen in hypertension, can revealan enlargement in the proximal aorta due to long-standing hypertension.Another example associated factor of ischemia that can be utilized asinput and/or analyzed by the system can be related to mitral valvecalcification, which can be diagnosed by identifying calcium in themitral valve.

As discussed herein, in some embodiments, the system can be configuredto utilize one or more inputs or variables for generating a globalischemia index, for example by inputting the like into a regressionmodel or other algorithm. In some embodiments, the system can beconfigured to use as input one or more radiomics features and/orimaging-based deep learning. In some embodiments, the system can beconfigured to utilize as input one or more of patient height, weight,sex, ethnicity, body surface, previous medication, genetics, and/or thelike.

In some embodiments, the system can be configured to analyze and/orutilize as input calcium, separate calcium densities, localizationcalcium to lumen, volume of calcium, and/or the like. In someembodiments, the system can be configured to analyze and/or utilize asinput contrast vessel attenuation. In particular, in some embodiments,the system can be configured to analyze and/or utilize as input averagecontrast in the lumen in the beginning of a segment and/or averagecontrast in the lumen at the end of that segment. In some embodiments,the system can be configured to analyze and/or utilize as input averagecontrast in the lumen in the beginning of the vessel to the beginning ofthe distal segment of that vessel, for example because the end can betoo small in some instances.

In some embodiments, the system can be configured to analyze and/orutilize as input plaque heterogeneity. In particular, in someembodiments, the system can be configured to analyze and/or utilize asinput calcified plaque volume versus and/or non-calcified plaque volume.In some embodiments, the system can be configured to analyze and/orutilize as input standard deviation of one or more of the 3 differentcomponents of plaque.

In some embodiments, the system can be configured to analyze and/orutilize as input one or more vasodilation metrics. In particular, insome embodiments, the system can be configured to analyze and/or utilizeas input the highest remodeling index of a plaque. In some embodiments,the system can be configured to analyze and/or utilize as input thehighest, average, and/or smallest thickness of plaque, and for examplefor its calcified and/or non-calcified components. In some embodiments,the system can be configured to analyze and/or utilize as input thehighest remodeling index and/or lumen area. In some embodiments, thesystem can be configured to analyze and/or utilize as input the lesionlength and/or segment length of plaque.

In some embodiments, the system can be configured to analyze and/orutilize as input bifurcation lesion, such as for example the presence ofabsence thereof. In some embodiments, the system can be configured toanalyze and/or utilize as input coronary dominance, for example leftdominance, right dominance, and/or codominance. In particular, in someembodiments, if left dominance, the system can be configured todisregard and/or weight less one or more right coronary metrics.Similarly, if right dominance, the system can be configured to disregardand/or weight less one or more left coronary metrics.

In some embodiments, the system can be configured to analyze and/orutilize as input one or more vascularization metrics. In particular, insome embodiments, the system can be configured to analyze and/or utilizeas input the volume of the lumen of one or more, some, or all vessels.In some embodiments, the system can be configured to analyze and/orutilize as input the volume of the lumen of one or more secondaryvessels, such as for example, non-right coronary artery (non-RCA), leftanterior descending artery (LAD) vessel, circumflex (CX) vessel, and/orthe like. In some embodiments, the system can be configured to analyzeand/or utilize as input the volume of vessel and/or volume of plaqueand/or a ratio thereof.

In some embodiments, the system can be configured to analyze and/orutilize as input one or more inflammation metrics. In particular, insome embodiments, the system can be configured to analyze and/or utilizeas input the average density of one or more pixels outside a lesion,such as for example 5 pixels and/or 3 or 4 pixels of 5, disregarding thefirst 1 or 2 pixels. In some embodiments, the system can be configuredto analyze and/or utilize as input the average density of one or morepixels outside a lesion including the first ⅔ of each vessel that is nota lesion or plaque. In some embodiments, the system can be configured toanalyze and/or utilize as input one or more pixels outside a lesionand/or the average of the same pixels on a 3 mm section above theproximal right coronary artery (R1) if there is no plaque in that place.In some embodiments, the system can be configured to analyze and/orutilize as input one or more ratios of any factors and/or variablesdescribed herein.

As described above, in some embodiments, the system can be configured toutilize one or more machine learning algorithms in identifying,deriving, and/or analyzing one or more inputs for generating the globalischemia index, including for example one or more direct contributors toischemia, early consequences of ischemia, late consequences of ischemia,associated factors with ischemia, and other test findings in relation toischemia. In some embodiments, one or more such machine learningalgorithms can provide fully automated quantification and/orcharacterization of such factors.

As an example, in some embodiments, the system can be configured toutilize one or more machine learning algorithms to identify, derive,and/or analyze inferior vena cava from one or more medical images.Measures of inferior vena cava can be of high importance in patientswith right-sided heart failure and tricuspid regurgitation.

In addition, in some embodiments, the system can be configured toutilize one or more machine learning algorithms to identify, derive,and/or analyze the interatrial septum from one or more medical images.Interatrial septum dimensions can be vital for patients undergoingleft-sided transcatheter procedures.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyzedescending thoracic aorta from one or more medical images. Measures ofdescending thoracic aorta can be of critical importance in patients withaortic aneurysms, and for population-based screening in long-timesmokers.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze thecoronary sinus from one or more medical images. Coronary sinusdimensions can be vital for patients with heart failure who areundergoing biventricular pacing. In some embodiments, by analyzing thecoronary sinus, the system can be configured to derive all or somemyocardium blood flow, which can be related to coronary volume,myocardium mass. In addition, in some embodiments, the system can beconfigured to analyze, derive, and/or identify hypertrophiccardiomyopathy (HCM), other hypertrophies, ischemia, and/or the like toderive ischemia and/or microvascular ischemia.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze theanterior mitral leaflet from one or more medical images. For a patientbeing considered for surgical or transcatheter mitral valve repair orreplacement, no current method currently exists to measure anteriormitral leaflet dimensions.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze the leftatrial appendage from one or more medical images. Left atrial appendagemorphologies are linked to stroke in patients with atrial fibrillation,but no automated characterization solution exists today.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze the leftatrial free wall mass from one or more medical images. No current methodexists to accurately measure left atrial free wall mass, which may beimportant in patients with atrial fibrillation.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze the leftventricular mass from one or more medical images. Certain methods ofmeasuring left ventricular hypertrophy as an adverse consequence ofhypertension rely upon echocardiography, which employs a 2D estimatedformula that is highly imprecise. 3D imaging by magnetic resonanceimaging (MRI) or computed tomography (CT) are much more accurate, butcurrent software tools are time-intensive and imprecise.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze the leftatrial volume from one or more medical images. Determination of leftatrial volume can improve diagnosis and risk stratification in patientswith and at risk of atrial fibrillation.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze the leftventricular volume from one or more medical images. Left ventricularvolume measurements can enable determination of individuals with heartfailure or at risk of heart failure.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze the leftventricular papillary muscle mass from one or more medical images. Nocurrent method currently exists to measure left ventricular papillarymuscle mass.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze theposterior mitral leaflet from one or more medical images. For patientsbeing considered for surgical or transcatheter mitral valve repair orreplacement, no current method currently exists to measure posteriormitral leaflet dimensions.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyzepulmonary veins from one or more medical images. Measures of pulmonaryvein dimensions can be of critical importance in patients with atrialfibrillation, heart failure and mitral regurgitation.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyzepulmonary arteries from one or more medical images. Measures ofpulmonary artery dimensions can be of critical importance in patientswith pulmonary hypertension, heart failure and pulmonary emboli.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze theright atrial free wall mass from one or more medical images. No currentmethod exists to accurately measure right atrial free wall mass, whichmay be important in patients with atrial fibrillation.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze theright ventricular mass from one or more medical images. Methods ofmeasuring right ventricular hypertrophy as an adverse consequence ofpulmonary hypertension and/or heart failure do not currently exist.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze theproximal ascending aorta from one or more medical images. Aorticaneurysms can require highly precise measurements of the aorta, whichare more accurate by 3D techniques such as CT and MRI. At present,current algorithms do not allow for highly accurate automatedmeasurements.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze theright atrial volume from one or more medical images. Determination ofright atrial volume can improve diagnosis and risk stratification inpatients with and at risk of atrial fibrillation.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze theright ventricular papillary muscle mass from one or more medical images.No current method currently exists to measure right ventricularpapillary muscle mass.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze theright ventricular volume from one or more medical images. Rightventricular volume measurements can enable determination of individualswith heart failure or at risk of heart failure.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, and/or analyze thesuperior vena cava from one or more medical images. No reliable methodexists to date to measure superior vena cava dimensions, which may beimportant in patients with tricuspid valve insufficiency and heartfailure.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms to identify, derive, analyze, segment,and/or quantify one or more cardiac structures from one or more medicalimages, such as the left and right ventricular volume (LVV, RVV), leftand right atrial volume (LAV, RAV), and/or left ventricular myocardialmass (LVM).

Further, in some embodiments, the system can be configured to utilizeone or more machine learning algorithms to identify, derive, analyze,segment, and/or quantify one or more cardiac structures from one or moremedical images, such as the proximal ascending and descending aorta(PAA, DA), superior and inferior vena cava (SVC, IVC), pulmonary artery(PA), coronary sinus (CS), right ventricular wall (RVW), and left atrialwall (LAW).

In addition, in some embodiments, the system can be configured toutilize one or more machine learning algorithms to identify, derive,analyze, segment, and/or quantify one or more cardiac structures fromone or more medical images, such as the left atrial appendage, leftatrial wall, coronary sinus, descending aorta, superior vena cava,inferior vena cava, pulmonary artery, right ventricular wall, sinuses ofValsalva, left ventricular volume, left ventricular wall, rightventricular volume, left atrial volume, right atrial volume, and/orproximal ascending aorta.

FIG. 20E is a flowchart illustrating an overview of an exampleembodiment(s) of a method for generating a global ischemia index for asubject and using the same to assist assessment of risk of ischemia forthe subject. As illustrated in FIG. 20E, in some embodiments, the systemcan be configured to access one or more medical images of a subject atblock 202, in any manner and/or in connection with any feature describedabove in relation to block 202. In some embodiments, the system isconfigured to identify one or more vessels, plaque, and/or fat in theone or more medical images at block 2002. For example, in someembodiments, the system can be configured to use one or more AI and/orML algorithms and/or other image processing techniques to identify oneor more vessels, plaque, and/or fat.

In some embodiments, the system at block 2004 is configured to analyzeand/or access one or more contributors to ischemia of the subject,including any contributors to ischemia described herein, for examplebased on the accessed one or more medical images and/or other medicaldata. In some embodiments, the system at block 2006 is configured toanalyze and/or access one or more consequences of ischemia of thesubject, including any consequences of ischemia described herein,including early and/or late consequences, for example based on theaccessed one or more medical images and/or other medical data. In someembodiments, the system at block 2008 is configured to analyze and/oraccess one or more associated factors to ischemia of the subject,including any associated factors to ischemia described herein, forexample based on the accessed one or more medical images and/or othermedical data. In some embodiments, the system at block 2010 isconfigured to analyze and/or access one or more results from othertesting, such as for example invasive testing, non-invasive testing,image-based testing, non-image based testing, and/or the like.

In some embodiments, the system at block 2012 can be configured togenerate a global ischemia index based on one or more parameters, suchas for example one or more contributors to ischemia, one or moreconsequences of ischemia, one or more associated factors to ischemia,one or more other testing results, and/or the like. In some embodiments,the system is configured to generate a global ischemia index for thesubject by generating a weighted measure of one or more parameters. Forexample, in some embodiments, the system is configured to weight one ormore parameters differently and/or equally. In some embodiments, thesystem can be configured weight one or more parameters logarithmically,algebraically, and/or utilizing another mathematical transform. In someembodiments, the system is configured to generate a weighted measureusing only some or all of the parameters.

In some embodiments, at block 2014, the system is configured to verifythe generated global ischemia index. For example, in some embodiments,the system is configured to verify the generated global ischemia indexby comparison to one or more blood flow parameters such as thosediscussed herein. In some embodiments, at block 2016, the system isconfigured to generate user assistance to help a user determine anassessment of risk of ischemia for the subject based on the generatedglobal ischemia index, for example graphically through a user interfaceand/or otherwise.

CAD Score(s)

Some embodiments of the systems, devices, and methods described hereinare configured to generate one or more coronary artery disease (CAD)scores representative of a risk of CAD for a particular subject. In someembodiments, the risk score can be generated by analyzing and/orcombining one or more aspects or characteristics relating to plaqueand/or cardiovascular features, such as for example plaque volume,plaque composition, vascular remodeling, high-risk plaque, lumen volume,plaque location (proximal v. middle v. distal), plaque location(myocardial v. pericardial facing), plaque location (at bifurcation ortrifurcation v. not at bifurcation or trifurcation), plaque location (inmain vessel v. branch vessel), stenosis severity, percentage coronaryblood volume, percentage fractional myocardial mass, percentile for ageand/or gender, constant or other correction factor to allow for controlof within-person, within-vessel, inter-plaque, plaque-myocardialrelationships, and/or the like. In some embodiments, a CAD risk score(s)can be generated based on automatic and/or dynamic analysis of one ormore medical images, such as for example a CT scan or an image obtainedfrom any other modality mentioned herein. In some embodiments, dataobtained from analyzing one or more medical images of a patient can benormalized in generating a CAD risk score(s) for that patient. In someembodiments, the systems, devices, and methods described herein can beconfigured to generate a CAD risk score(s) for different vessels,vascular territories, and/or patients. In some embodiments, the systems,devices, and methods described herein can be configured to generate agraphical visualization of risk of CAD of a patient based on a vesselbasis, vascular territory basis, and/or patient basis. In someembodiments, based on the generated CAD risk score(s), the systems,methods, and devices described herein can be configured to generate oneor more recommended treatments for a patient. In some embodiments, thesystem can be configured to utilize a normalization device, such asthose described herein, to account for differences in scan results (suchas for example density values, etc.) between different scanners, scanparameters, and/or the like.

In some embodiments, the systems, devices, and methods described hereincan be configured to assess patients with suspected coronary arterydisease (CAD) by use of one or more of a myriad of different diagnosticand prognostic tools. In particular, in some embodiments, the systems,devices, and methods described herein can be configured to use a riskscore for cardiovascular care for patients without known CAD.

As a non-limiting example, in some embodiments, the system can beconfigured to generate an Atherosclerotic Cardiovascular Disease (ASCVD)risk score, which can be based upon a combination of age, gender, race,blood pressure, cholesterol (total, HDL and LDL), diabetes status,tobacco use, hypertension, and/or medical therapy (such as for example,statin and aspirin).

As another non-limiting example, in some embodiments, the system can beconfigured to generate a Coronary Artery Calcium Score (CACS), which canbe based upon a non-contrast CT scan wherein coronary arteries arevisualized for the presence of calcified plaque. In some embodiments, anAgatston (e.g., a measure of calcium in a coronary CT scan) score may beused to determine the CACS. In particular, in some embodiments, a CACSscore can be calculated by: Agatston score = surface area × Hounsfieldunit density (with brighter plaques with higher density receiving ahigher score). However, in some embodiments, there may be certainlimitations with a CACS score. For example, in some embodiments, becausesurface area to volume ratio decreases as a function of the overallvolume, more spherical plaques can be incorrectly weighted as lesscontributory to the Agatston score. In addition, in some embodiments,because Hounsfield unit density is inversely proportional to risk ofmajor adverse cardiac events (MACE), weighting the HU density higher canscore a lower risk plaque as having a higher score. Moreover, in someembodiments, 2.5-3 mm thick CT “slices” can miss smaller calcifiedplaques, and/or no use of beta blocker results in significant motionartifact, which can increase the calcium score due to artifact.

In some embodiments, for symptomatic patients undergoing coronary CTangiography, the system can be configured to generate and/or utilize oneor more additional risk scores, such as a Segment Stenosis Score,Segment Involvement Score, Segments-at-Risk Score, Duke PrognosticIndex, CTA Score, and/or the like. More specifically, in someembodiments, a Segment Stenosis Score weights specific stenoses (0=0%,1=1-24%, 2=25-49%, 3=50-69%, 4=>70%) across the entire 18 coronarysegment, resulting in a total possible score of 72. In some embodiments,a Segment Involvement Score counts the number of plaques located in the18 segments and has a total possible score of 18.

In some embodiments, a Segments-at-Risk Score reflects the potentialsusceptibility of all distal coronary segments subtended by severeproximal plaque. Thus, in some embodiments, all segments subtended bysevere proximal plaque can be scored as severe as well, then summatedover 18 segments to create a segment-at-risk score. For example, if theproximal portion of the LCx is considered severely obstructive, thesegments-at-risk score for the LCx can be proximal circumflex (=3) + midcircumflex (=3) + distal circumflex (=3) + proximal obtuse marginal(=3) + mid obtuse marginal (=3) + distal obtuse marginal (=3), for atotal circumflex segments-at-risk score of 18. In this individual, ifthe LAD exhibits mild plaque in the proximal portion (=1) and moderateplaque in the midportion (=2), the LAD segments-at-risk score can be 3.If the RCA exhibits moderate plaque in the proximal portion (=3), theRCA segments-at-risk score can be 2. Thus, for this individual, thetotal segments-at-risk score can be 23 out of a possible 48.

In some embodiments, a Duke Prognostic Index can be a reflection of thecoronary artery plaque severity considering plaque location. In someembodiments, a modified Duke CAD index can consider overall plaqueextent relating it to coexistent plaque in the left main or proximalLAD. In some embodiments, using this scoring system, individuals can becategorized into six distinct groups: no evident coronary artery plaque;≥2 mild plaques with proximal plaque in any artery or 1 moderate plaquein any artery; 2 moderate plaques or 1 severe plaque in any artery; 3moderate coronary artery plaques or 2 severe coronary artery plaques orisolated severe plaque in the proximal LAD; 3 severe coronary arteryplaques or 2 severe coronary artery plaques with proximal LAD plaque;moderate or severe left main plaque.

In some embodiments, a CT angiography (CTA) Score can be calculated bydetermining CAD in each segment, such as for example proximal RCA, midRCA, distal RCA, R-PDA, R-PLB, left main, proximal LAD, mid LAD, distalLAD, D1, D2, proximal LCX, distal LCX, IM/AL, OM, L-PL, L-PDA, and/orthe like. In particular, for each segment, when plaque is absent, thesystem can be configured to assign a score of 0, and when plaque ispresent, the system can be configured to assign a score of 1.1, 1.2 or1.3 according to plaque composition (such as calcified, non-calcifiedand mixed plaque, respectively). In some embodiments, these scores canbe multiplied by a weight factor for the location of the segment in thecoronary artery tree (for example, 0.5 - 6 according to vessel, proximallocation and system dominance). In some embodiments, these scores canalso be multiplied by a weight factor for stenosis severity (forexample, 1.4 for ≥50% stenosis and 1.0 for stenosis <50%). In someembodiments, the final score can be calculated by addition of theindividual segment scores.

In some embodiments, the systems, devices, and methods described hereincan be configured to utilize and/or perform improved quantificationand/or characterization of many parameters on CT angiography that werepreviously very difficult to measure. For example, in some embodiments,the system can be configured to determine stenosis severity leveraging aproximal/distal reference and report on a continuous scale, for examplefrom 0-100%, by diameter, area, and/or volumetric stenosis. In someembodiments, the system can be configured to determine total atheromaburden, reported in volumes or as a percent of the overall vessel volume(PAV), including for example non-calcified plaque volume (for example,as a continuous variable, ordinal variable or single variable),calcified plaque volume (for example, as a continuous variable, ordinalvariable or single variable), and/or mixed plaque volume (for example,as a continuous variable, ordinal variable or single variable).

In some embodiments, the system can be configured to determine lowattenuation plaque, for example reported either as yes/no binary orcontinuous variable based upon HU density. In some embodiments, thesystem can be configured to determine vascular remodeling, for examplereported as ordinal negative, intermediate or positive (for example,<0.90, 0.90-1.10, or >1.0) or continuous. In some embodiments, thesystem can be configured to determine and/or analyze various locationsof plaque, such as for example proximal/mid/distal, myocardial facingvs. pericardial facing, at bifurcation v. not at bifurcation, in mainvessel vs. branch vessel, and/or the like.

In some embodiments, the system can be configured to determinepercentage coronary blood volume, which can report out the volume of thelumen (and downstream subtended vessels in some embodiments) as afunction of the entire coronary vessel volume (for example, eithermeasured or calculated as hypothetically normal). In some embodiments,the system can be configured to determine percentage fractionalmyocardial mass, which can relate the coronary lumen or vessel volume tothe percentage downstream subtended myocardial mass.

In some embodiments, the system can be configured to determine therelationship of all or some of the above to each other, for example on aplaque-plaque basis to influence vessel behavior/risk or on avessel-vessel basis to influence patient behavior/risk. In someembodiments, the system can be configured to utilize one or morecomparisons of the same, for example to normal age- and/or gender-basedreference values.

In some embodiments, one or more of the metrics described herein can becalculated on a per-segment basis. In some embodiments, one or more ofthe metrics calculated on a per-segment basis can then summed across avessel, vascular territory, and/or patient level. In some embodiments,the system can be configured to visualize one or more of such metrics,whether on a per-segment basis and/or on a vessel, vascular territory,and/or patient basis, on a geographical scale. For example, in someembodiments, the system can be configured to visualize one or more suchmetrics on a graphical scale using 3D and/or 4D histograms.

Further, in some embodiments, cardiac CT angiography enablesquantitative assessment of a myriad of cardiovascular structures beyondthe coronary arteries, which may both contribute to coronary arterydisease as well as other cardiovascular diseases. For example, thesemeasurements can include those of one or more of: (1) left ventricle -e.g., left ventricular mass, left ventricular volume, left ventricleHounsfield unit density as a surrogate marker of ventricular perfusion;(2) right ventricle - e.g., right ventricular mass, right ventricularvolume; (3) left atrium - e.g., volume, size, geometry; (4) rightatrium - e.g., volume, size, geometry; (5) left atrial appendage - e.g.,morphology (e.g., chicken wing, windsock, etc.), volume, angle, etc.;(6) pulmonary vein - e.g., size, shape, angle of takeoff from the leftatrium, etc.; (7) mitral valve - e.g., volume, thickness, shape, length,calcification, anatomic orifice area, etc.; (8) aortic valve - e.g.,volume, thickness, shape, length, calcification, anatomic orifice area,etc.; (9) tricuspid valve - e.g., volume, thickness, shape, length,calcification, anatomic orifice area, etc.; (10) pulmonic valve - e.g.,volume, thickness, shape, length, calcification, anatomic orifice area,etc.; (11) pericardial and pericoronary fat -e.g., volume, attenuation,etc.; (12) epicardial fat - e.g., volume, attenuation, etc.; (13)pericardium - e.g., thickness, mass, volume; and/or (14) aorta - e.g.,dimensions, calcifications, atheroma.

Given the multitude of measurements that can help characterizecardiovascular risk, certain existing scores can be limited in theirholistic assessment of the patient and may not account for many keyparameters that may influence patient outcome. For example, certainexisting scores may not take into account the entirety of data that isneeded to effectively prognosticate risk. In addition, the data thatwill precisely predict risk can be multi-dimensional, and certain scoresdo not consider the relationship of plaques to one another, or vessel toone another, or plaques-vessels-myocardium relationships or all of thoserelationships to the patient-level risk. Also, in certain existingscores, the data may categorize plaques, vessels and patients, thuslosing the granularity of pixel-wise data that are summarized in thesescores. In addition, in certain existing scores, the data may notreflect the normal age-and gender-based reference values as a benchmarkfor determining risk. Moreover, certain scores may not consider a numberof additional items that can be gleaned from quantitative assessment ofcoronary artery disease, vascular morphology and/or downstreamventricular mass. Further, within-person relationships of plaques,segments, vessels, vascular territories may not considered withincertain risk scores. Furthermore, no risk score to date that utilizesimaging normalizes these risks to a standard that accounts fordifferences in scanner make/model, contrast type, contrast injectionrate, heart rate / cardiac output, patient characteristics,contrast-to-noise ratio, signal-to-noise ratio, and/or image acquisitionparameters (for example, single vs. dual vs. spectral energy imaging;retrospective helical vs. prospective axial vs. fast-pitch helical;whole-heart imaging versus non-whole-heart [i.e., non-volumetric]imaging; etc.). In some embodiments described herein, the systems,methods, and devices overcome such technical shortcomings.

In particular, in some embodiments, the systems, devices, and methodsdescribed herein can be configured to generate and/or a novel CAD riskscore that addresses the aforementioned limitations by considering oneor more of: (1) total atheroma burden, normalized for density, such asabsolute density or Hounsfield unit (HU) density (e.g., can becategorized as total volume or relative volume, i.e., plaque volume /vessel volume × 100%); (2) plaque composition by density or HU density(e.g., can be categorized continuously, ordinally or binarily); (3) lowattenuation plaque (e.g., can be reported as yes/no binary or continuousvariable based upon density or HU density); (4) vascular remodeling(e.g., can be reported as ordinal negative, intermediate or positive(<0.90, 0.90-1.10, or >1.0) or continuous); (5) plaque location -proximal v. mid v. distal; (6) plaque location - which vessel orvascular territory; (7) plaque location - myocardial facing v.pericardial facing; (8) plaque location - at bifurcation v. not atbifurcation; (9) plaque location - in main vessel v. branch vessel; (10)stenosis severity; (11) percentage coronary blood volume (e.g., thismetric can report out the volume of the lumen (and downstream subtendedvessels) as a function of the entire coronary vessel volume (e.g.,either measured or calculated as hypothetically normal)); (12)percentage fractional myocardial mass (e.g., this metric can relate thecoronary lumen or vessel volume to the percentage downstream subtendedmyocardial mass); (13) consideration of normal age- and/or gender-basedreference values; and/or (14) statistical relationships of all or someof the above to each other (e.g., on a plaque-plaque basis to influencevessel behavior/risk or on a vessel-vessel basis to influence patientbehavior/risk).

In some embodiments, the system can be configured to determine abaseline clinical assessment(s), including for such factors as one ormore of: (1) age; (2) gender; (3) diabetes (e.g., presence, duration,insulin-dependence, history of diabetic ketoacidosis, end-organcomplications, which medications, how many medications, and/or thelike); (4) hypertension (e.g., presence, duration, severity, end-organdamage, left ventricular hypertrophy, number of medications, whichmedications, history of hypertensive urgency or emergency, and/or thelike); (5) dyslipidemia (e.g., including low-density lipoprotein (LDL),triglycerides, total cholesterol, lipoprotein(a) Lp(a), apolipoprotein B(ApoB), and/or the like); (6) tobacco use (e.g., including what type,for what duration, how much use, and/or the like); (7) family history(e.g., including which relative, at what age, what type of event, and/orthe like); (8) peripheral arterial disease (e.g., including what type,duration, severity, end-organ damage, and/or the like); (9)cerebrovascular disease (e.g., including what type, duration, severity,end-organ damage, and/or the like); (10) obesity (e.g., including howobese, how long, is it associated with other metabolic derangements,such as hypertriglyceridemia, centripetal obesity, diabetes, and/or thelike); (11) physical activity (e.g., including what type, frequency,duration, exertional level, and/or the like); and/or (12) psychosocialstate (e.g., including depression, anxiety, stress, sleep, and/or thelike).

In some embodiments, a CAD risk score is calculated for each segment,such as for example for segment 1, segment 2, or for some or allsegments. In some embodiments, the score is calculated by combining(e.g., by multiplying or applying any other mathematical transform orgenerating a weighted measure of) one or more of: (1) plaque volume(e.g., absolute volume such as in mm3 or PAV; may be weighted); (2)plaque composition (e.g., NCP/CP, Ordinal NCP/Ordinal CP; Continuous;may be weighted); (3) vascular remodeling (e.g.,Positive/Intermediate/Negative; Continuous; may be weighted); (4)high-risk plaques (e.g., positive remodeling + low attenuation plaque;may be weighted); (5) lumen volume (e.g., may be absolute volume such asin mm3 or relative to vessel volume or relative to hypothetical vesselvolume; may be weighted); (6) location - proximal / mid / distal (may beweighted); (7) location - myocardial vs. pericardial facing (may beweighted); (8) location - at bifurcation / trifurcation vs. not atbifurcation / trifurcation (may be weighted); (9) location - in mainvessel vs. branch vessel (may be weighted); (10) stenosis severity(e.g., ><70%, <>50%, 1-24, 25-49, 50-69, >70%; 0, 1-49, 50-69, >70%;continuous; may use diameter, area or volume; may be weighted); (11)percentage Coronary Blood Volume (may be weighted); (12) percentagefractional myocardial mass (e.g., may include total vessel volume-to-LVmass ratio; lumen volume-to-LV mass ratio; may be weighted); (13)percentile for age- and gender; (14) constant / correction factor (e.g.,to allow for control of within-person, within-vessel, inter-plaque,and/or plaque-myocardial relationships). As a non-limiting example, ifSegment 1 has no plaque, then it can be weighted as 0 in someembodiments.

In some embodiments, to determine risk (which can be defined as risk offuture myocardial infarction, major adverse cardiac events, ischemia,rapid progression, insufficient control on medical therapy, progressionto angina, and/or progression to need of target vesselrevascularization), all or some of the segments are added up on aper-vessel, per-vascular territory and per-patient basis. In someembodiments, by using plots, the system can be configured to visualizeand/or quantify risk based on a vessel basis, vascular territory basis,and patient-basis.

In some embodiments, the score can be normalized in a patient- andscan-specific manner by considering items such as for example: (1)patient body mass index; (2) patient thorax density; (3) scannermake/model; (4) contrast density along the Z-axis and along vesselsand/or cardiovascular structures; (5) contrast-to-noise ratio; (6)signal-to-noise ratio; (7) method of ECG gating (e.g., retrospectivehelical, prospective axial, fast-pitch helical); (8) energy acquisition(e.g., single, dual, spectral, photon counting); (9) heart rate; (10)use of pre-CT medications that may influence cardiovascular structures(e.g., nitrates, beta blockers, anxiolytics); (11) mA; and/or (12) kvp.

In some embodiments, without normalization, cardiovascular structures(coronary arteries and beyond) may have markedly different Hounsfieldunits for the same structure (e.g., if 100 vs. 120 kvp is used, a singlecoronary plaque may exhibit very different Hounsfield units). Thus, insome embodiments, this “normalization” step is needed, and can beperformed based upon a database of previously acquired images and/or canbe performed prospectively using an external normalization device, suchas those described herein.

In some embodiments, the CAD risk score can be communicated in severalways by the system to a user. For example, in some embodiments, agenerated CAD risk score can be normalized to a scale, such as a 100point scale in which 90-100 can refer to excellent prognosis, 80-90 forgood prognosis, 70-80 for satisfactory prognosis, 60-70 for belowaverage prognosis, <60 for poor prognosis, and/or the like. In someembodiments, the system can be configured to generate and/or report to auser based on the CAD risk score(s) vascular age vs. biological age ofthe subject. In some embodiments, the system can be configured tocharacterize risk of CAD of a subject as one or more of normal, mild,moderate, and/or severe. In some embodiments, the system can beconfigured to generate one or more color heat maps based on a generatedCAD risk score, such as red, yellow, green, for example in ordinal orcontinuous display. In some embodiments, the system can be configured tocharacterize risk of CAD for a subject as high risk vs. non-high-risk,and/or the like.

As a non-limiting example, in some embodiments, the generated CAD riskscore for Lesion 1 can be calculated as Vol × Composition (HU) × RI ×HRP × Lumen Volume × Location × Stenosis% × %CBV × %FMM × Age-/GenderNormal Value % × Correction Constant) × Correction factor for scan- andpatient-specific parameters × Normalization factor to communicateseverity of findings. Similarly, in some embodiments, the generated CADrisk score for Lesion 2 can be calculated as Vol × Composition (HU) × RI× HRP × Lumen Volume × Location × Stenosis% × %CBV × %FMM × Age-/GenderNormal Value % × Correction Constant) × Correction factor for scan- andpatient-specific parameters × Normalization factor to communicateseverity of findings. In some embodiments, the generated CAD risk scorefor Lesion 3 can be calculated as Vol × Composition (HU) × RI × HRP ×Lumen Volume × Location × Stenosis% × %CBV × %FMM × Age-/Gender NormalValue % × Correction Constant) × Correction factor for scan-andpatient-specific parameters × Normalization factor to communicateseverity of findings. In some embodiments, the generated CAD risk scorefor Lesion 4 can be calculated as Vol × Composition (HU) × RI X HRP ×Lumen Volume × Location × Stenosis% × %CBV × %FMM × Age-/Gender NormalValue % X Correction Constant) X Correction factor for scan-andpatient-specific parameters X Normalization factor to communicateseverity of findings. In some embodiments, a CAD risk score cansimilarly be generated for any other lesions.

In some embodiments, the CAD risk score can be adapted to other diseasestates within the cardiovascular system, including for example: (1)coronary artery disease and its downstream risk (e.g., myocardialinfarction, acute coronary syndromes, ischemia, rapid progression,progression despite medical therapy, progression to angina, progressionto need for target vessel revascularization, and/or the like); (2) heartfailure; (3) atrial fibrillation; (4) left ventricular hypertrophy andhypertension; (5) aortic aneurysm and/or dissection; (6) valvularregurgitation or stenosis; (7) sudden coronary artery dissection, and/orthe like.

FIG. 21 is a flowchart illustrating an overview of an exampleembodiment(s) of a method for generating a coronary artery disease (CAD)Score(s) for a subject and using the same to assist assessment of riskof CAD for the subject. As illustrated in FIG. 21 , in some embodiments,the system is configured to conduct a baseline clinical assessment of asubject at block 2102. In particular, in some embodiments, the systemcan be configured to take into account one or more clinical assessmentfactors associated with the subject, such as for example age, gender,diabetes, hypertension, dyslipidemia, tobacco use, family history,peripheral arterial disease, cerebrovascular disease, obesity, physicalactivity, psychosocial state, and/or any details of the foregoingdescribed herein. In some embodiments, one or more baseline clinicalassessment factors can be accessed by the system from a database and/orderived from non-image-based and/or image-based data.

In some embodiments, at block 202, the system can be configured toaccess one or more medical images of the subject at block 202, in anymanner and/or in connection with any feature described above in relationto block 202. In some embodiments, the system is configured to identifyone or more segments, vessels, plaque, and/or fat in the one or moremedical images at block 2104. For example, in some embodiments, thesystem can be configured to use one or more AI and/or ML algorithmsand/or other image processing techniques to identify one or moresegments, vessels, plaque, and/or fat.

In some embodiments, the system at block 2106 is configured to analyzeand/or access one or more plaque parameters. For example, in someembodiments, one or more plaque parameters can include plaque volume,plaque composition, plaque attenuation, plaque location, and/or thelike. In particular, in some embodiments, plaque volume can be based onabsolute volume and/or PAV. In some embodiments, plaque composition canbe determined by the system based on density of one or more regions ofplaque in a medical image, such as absolute density and/or Hounsfieldunit density. In some embodiments, the system can be configured tocategorize plaque composition binarily, for example as calcified ornon-calcified plaque, and/or continuously based on calcification levelsof plaque. In some embodiments, plaque attenuation can similarly becategorized binarily by the system, for example as high attenuation orlow attenuation based on density, or continuously based on attenuationlevels of plaque. In some embodiments, plaque location can becategorized by the system as one or more of proximal, mid, or distalalong a coronary artery vessel. In some embodiments, the system cananalyze plaque location based on the vessel in which the plaque islocated. In some embodiments, the system can be configured to categorizeplaque location based on whether it is myocardial facing, pericardialfacing, located at a bifurcation, located at a trifurcation, not locatedat a bifurcation, and/or not located at a trifurcation. In someembodiments, the system can be configured to analyze plaque locationbased on whether it is in a main vessel or in a branch vessel.

In some embodiments, the system at block 2108 is configured to analyzeand/or access one or more vessel parameters, such as for examplestenosis severity, lumen volume, percentage of coronary blood volume,percentage of fractional myocardial mass, and/or the like. In someembodiments, the system is configured to categorize or determinestenosis severity based on one or more predetermined ranges ofpercentage stenosis, for example based on diameter, area, and/or volume.In some embodiments, the system is configured to determine lumen volumebased on absolute volume, volume relative to a vessel volume, volumerelative to a hypothetical volume, and/or the like. In some embodiments,the system is configured to determine percentage of coronary bloodvolume based on determining a volume of lumen as a function of an entirecoronary vessel volume. In some embodiments, the system is configured todetermine percentage of fractional myocardial mass as a ratio of totalvessel volume to left ventricular mass, a ratio of lumen volume to leftventricular mass, and/or the like.

In some embodiments, the system at block 2110 is configured to analyzeand/or access one or more clinical parameters, such as for examplepercentile condition for age, percentile condition for gender of thesubject, and/or any other clinical parameter described herein.

In some embodiments, the system at block 2112 is configured to generatea weighted measure of one or more parameters, such as for example one ormore plaque parameters, one or more vessel parameters, and/or one ormore clinical parameters. In some embodiments, the system is configuredto generate a weighted measure of one or more parameters for eachsegment. In some embodiments, the system can be configured to generatethe weighted measure logarithmically, algebraically, and/or utilizinganother mathematical transform. In some embodiments, the system can beconfigured to generate the weighted measure by applying a correctionfactor or constant, for example to allow for control of within-person,within-vessel, inter-plaque, and/or plaque-myocardial relationships.

In some embodiments, the system at block 2114 is configured to generateone or more CAD risk scores for the subject. For example, in someembodiments, the system can be configured to generate a CAD risk scoreon a per-vessel, per-vascular territory, and/or per-subject basis. Insome embodiments, the system is configured to generate one or more CADrisk scores of the subject by combining the generated weighted measureof one or more parameters.

In some embodiments, the system at block 2116 can be configured tonormalize the generated one or more CAD scores. For example, in someembodiments, the system can be configured to normalize the generated oneor more CAD scores to account for differences due to the subject,scanner, and/or scan parameters, including those described herein.

In some embodiments, the system at block 2118 can be configured togenerate a graphical plot of the generated one or more per-vessel,per-vascular territory, or per-subject CAD risk scores for visualizingand quantifying risk of CAD for the subject. For example, in someembodiments, the system can be configured to generate a graphical plotof one or more CAD risk scores on a per-vessel, per-vascular, and/orper-subject basis. In some embodiments, the graphical plot can include a2D, 3D, or 4D representation, such as for example a histogram.

In some embodiments, the system at block 2120 can be configured toassist a user to generate an assessment of risk of CAD for the subjectbased the analysis. For example, in some embodiments, the system can beconfigured to generate a scaled CAD risk score for the subject. In someembodiments, the system can be configured to determine a vascular agefor the subject. In some embodiments, the system can be configured tocategorize risk of CAD for the subject, for example as normal, mild,moderate, or severe. In some embodiments, the system can be configuredto generate one or more colored heart maps. In some embodiments, thesystem can be configured to categorize risk of CAD for the subject ashigh risk or low risk.

Treat to the Image

Some embodiments of the systems, devices, and methods described hereinare configured to track progression of a disease, such as a coronaryartery disease (CAD), based on image analysis and use the results ofsuch tracking to determine treatment for a patient. In other words, insome embodiments, the systems, methods, and devices described herein areconfigured to treat a patient or subject to the image. In particular, insome embodiments, the system can be configured to track progression of adisease in response to a medical treatment by analyzing one or moremedical images over time and use the same to determine whether themedical treatment is effective or not. For example, in some embodiments,if the prior medical treatment is determined to be effectiveness basedon tracking of disease progression based on image analysis, the systemcan be configured to propose continued use of the same treatment. On theother hand, in some embodiments, if the prior medical treatment isdetermined to be neutral or non-effective based on tracking of diseaseprogression based on image analysis, the system can be configured topropose a modification of the prior treatment and/or a new treatment forthe subject. In some embodiments, the treatment can include medication,lifestyle changes or actions, and/or revascularization procedures.

In particular, some embodiments of the systems, devices, and methodsdescribed herein are configured to determine one or more of theprogression, regression or stabilization, and/or destabilization ofcoronary artery disease or other vascular disease over time in a mannerthat will reduce adverse coronary events. For example, in someembodiments, the systems, devices, and methods described herein areconfigured to provide medical analysis and/or treatment based on plaqueattenuation tracking. In some embodiments, the systems, devices, andmethods described herein can be configured to utilize a computer systemand/or an artificial intelligence platform to track the attenuation ofplaque, wherein an automatically detected transformation from lowattenuation plaque to high attenuation plaque on a medical image, ratherthan regression of plaque, can be used as the main basis for generatinga plaque attenuation score or status, which can be representative of therate of progression and/or rate of increased/decreased risk of coronarydisease. As such, in some embodiments, the systems, devices, and methodsdescribed herein can be configured to provide response assessment ofmedical therapy, lifestyle interventions, and/or coronaryrevascularization along the life course of an individual.

In some embodiments, the system can be configured to utilize computedtomography angiography (CCTA). Generally speaking, computed tomographyangiography (CCTA) can enable evaluation of presence, extent, severity,location and/or type of atherosclerosis in the coronary and otherarteries. These factors can change with medical therapy and lifestylemodifications and coronary interventions. As a non-limiting example, insome cases, Omega-3 fatty acids, after 38.6 months can lower high-riskplaque prevalence, number of high-risk plaques, and/or napkin-ring sign.Also, the CT density of plaque can be higher in omega-3 fatty acidsgroup. As another non-limiting example, in some cases, icosapent ethylcan result in reduced low attenuation plaque (LAP) volume by 17% andoverall plaque volume by 9% compared to baseline and placebo. Inaddition, as another non-limiting example, in some cases of HIV positivepatients, higher non-calcified and high-risk plaque burden onanti-retroviral therapy can be higher and can involve highercardiovascular risk. Further, as another non-limiting example, in somecases of patients taking statins, there can be slower rate of percentatheroma progression with more rapid progression of calcified percentatheroma volume. Other changes in plaque can also occur due to someother exposure. Importantly, in some instances, patients may often betaking combinations of these medications and/or living healthy orunhealthy lifestyles that may contribute multi-factorially to thechanges in plaque over time in a manner that is not predictable, but canbe measurable, for example utilizing one or more embodiments describedherein.

In some embodiments, the systems, methods, and devices described hereincan be configured to analyze dichotomous and/or categorical changes inplaque (e.g., from non-calcified to calcified, high-risk tonon-high-risk, and/or the like) and burden of plaque (e.g., volume,percent atheroma volume, and/or the like), as well as analyze serialcontinuous changes over time. In addition, in some embodiments, thesystems, methods, and devices described herein can be configured toleverage the continuous change of a plaque’s features as a longitudinalmethod for guiding need for intensification of medical therapy, changein lifestyle, and/or coronary revascularization. Further, in someembodiments, the systems, methods, and devices described herein can beconfigured to leverage the difference in these changes over time as amethod to guide therapy in a manner that improves patient-specificevent-free survival.

As such, in some embodiments, the systems, methods, and devicesdescribed herein can be configured to determine the progression,regression or stabilization, and/or destabilization of coronary arterydisease and/or other vascular disease over time, for example in responseto a medical treatment, in a manner that will reduce adverse coronaryevents. In particular, in some embodiments, the systems, methods, anddevices described herein can be configured to analyze the density /signal intensity, vascular remodeling, location of plaques, plaquevolume / disease burden, and/or the like. In some embodiments, thesystem can be configured to utilize a normalization device, such asthose described herein, to account for differences in scan results (suchas for example density values, etc.) between different scanners, scanparameters, and/or the like.

In some embodiments, the system can be configured to track imagingdensity (CT) and/or signal intensity (MRI) of coronary atheroscleroticlesions over time by serial imaging. In some embodiments, the system canbe configured to leverage directionality changes in coronary lesionsover time (e.g. lower-to-higher CT density, higher-to-even higher CTdensity, etc.) as measurements of stabilization of plaque. In someembodiments, the system can be configured to leverage directionalitychanges to link to risk of disease events (e.g., high CT density isassociated with lower risk of heart attack). In some embodiments, thesystem can be configured to guide decision making as to whether to addanother medication / intensity medical therapy. For example, if there isno change in density / signal intensity for a patient after 1 year, thesystem can be configured to propose addition of another medication. Insome embodiments, the system can be configured to guide decision makingin the above manner in order to reduce adverse coronary events (e.g.,acute coronary syndrome, rapid progression, ischemia, and/or the like).

FIG. 22A illustrates an example(s) of tracking the attenuation of plaquefor analysis and/or treatment of coronary artery and/or other vasculardisease. As a non-limiting example, FIG. 22A illustrates example crosssections of arteries from a CT image. In the illustrated exampleembodiment, the yellow circles are the lumen, the orange circles are theouter vessel wall and everything in between is plaque tissue or similar.In the illustrated example embodiment, the “high-risk plaques” by CT areindicated to the left, where they are classified as such by having lowattenuation plaque (e.g., <30 Hounsfield units) and positive (>1)vascular remodeling (e.g., cross-sectional area or diameter at the siteof maximum plaque compared to cross-sectional area at the most proximalnormal appearing cross-section). In some embodiments, positive arterialremodeling can be defined as >1.05 or >1.10.

As illustrated in the example embodiment of FIG. 22A, in someembodiments, plaques can be of continuously different density. In theleft most cross-section of the illustrated example embodiment, theplaque is black, and turns progressively gray and then lighter and thenbrighter until it becomes very bright white, with a Hounsfield unitdensity of >1000 in the right most cross-section of the illustratedexample embodiment. In some embodiments, this density can be reportedout continuously as Hounsfield unit densities or other depending on theacquisition mode of the CT image, which can include single-energy, dualenergy, spectral, and/or photon counting imaging.

In some embodiments, using imaging methods (e.g., by CT), darker plaques(e.g., with lower Hounsfield unit densities) can represent higher risk(e.g., of myocardial infarction, of causing ischemia, of progressingrapidly, and/or the like), while brighter plaques (e.g., with higherHounsfield unit density) can represent lower risk.

In some embodiments, the system is configured to leverage the continuousscale of the plaque composition density as a marker for increasedstabilization of plaque after treatment, and to leverage thisinformation to continually update prognostic risk stratification forfuture coronary events (e.g., acute coronary syndromes, ischemia, etc.).Thus, in some embodiments, an individual’s risk of a heart attack can bedependent on the density of the plaque, and changes in the density aftertreatment can attenuate that risk, increase that risk, and/or have noeffect on risk.

In some embodiments, the system can be configured to generate and/orsuggest treatment in a number of different forms, which may include:medications (e.g., statins, human immunodeficiency virus (HIV)medications, icosapent ethyl, bempedoic acid, rivaroxaban, aspirin,proprotein convertase subtilisin/kexin type 9 (PCSK-9) inhibitors,inclisiran, sodium-glucose cotransporter-2 (SGLT-2) inhibitors,glucagon-like peptide-1 (GLP-1) receptor agonists, low-densitylipoprotein (LDL) apheresis, etc.); lifestyle (increased exercise,aerobic exercise, anaerobic exercise, cessation of smoking, changes indiet, etc.); and/or revascularization (after bypass grafting, stenting,bioabsorbable scaffolds, etc.).

In some embodiments, the system can be configured to generate and/orprovide a “treat to the image” continuous approach that offersclinicians and patients a method for following plaque changes over timeto ensure that the plaque is stabilizing and the prognosis is improving.For example, in some embodiments, a patient may be started on a statinmedication after their CT scan. Over time (e.g., months), a plaque maychange in Hounsfield unit density from 30 to 45 HUs. In someembodiments, this may represent a beneficial outcome of plaquestabilization and connote the efficacy of the statin medications on theplaque. Alternatively, over time, a plaque may not change in Hounsfieldunit density, staying at 30 HU over time. In this case, in someembodiments, this may represent an adverse outcome wherein the statinmedication is ineffective in stabilizing the plaque. In someembodiments, should a plaque not stabilize to medical therapy (e.g., HUdensity remains low, or is very slow to rise), then another medication(e.g., PCSK-9 inhibitor) may be added as the constancy in the HU ca be atitratable biomarker that is used to guide medical therapyintensification and, ultimately, improve patient outcomes (e.g., byreducing myocardial infarction, rapid progression, ischemia, and/orother adverse event).

In some embodiments, densities of plaques may be influenced by a numberof factors that can include one or more of: scanner type, imageacquisition parameters (e.g., mA, kVp, etc.), energy (e.g., single-,dual-, spectral, photon counting, etc.), gating (e.g., axial vs.retrospective helical, etc.), contrast, age, patient body habitus,surrounding cardiac structures, plaque type (e.g., calcium may causepartial volume artifact, etc.), and/or others. As such, in someembodiments, the system can be configured to normalize one or more ofthese factors to further standardize comparisons in plaque types overtime.

In some embodiments, the system can be configured to track vascularremodeling of coronary atherosclerotic lesions over time using imageanalysis techniques. In some embodiments, the system can be configuredto leverage directionality changes in remodeling (e.g., outward,intermediate, inward, and/or the like). In some embodiments, the systemcan be configured to evaluate directionality on a patient, vessel,segment, lesion and/or cross section basis. In some embodiments, thesystem can be configured to leverage directionality changes to link torisk of disease events. For example, in some embodiments, more outwardremodeling can be indicative of a higher risk of heart attack, and/orthe like. In some embodiments, the system can be configured to guidedecision making as to whether to add another medication / intensifymedical therapy and/or perform coronary revascularization based uponworsening or new positive remodeling. In some embodiments, the systemcan be configured to guide decision making in the above manner in orderto reduce adverse coronary events (e.g., acute coronary syndrome, rapidprogression, ischemia, and/or the like).

In some embodiments, a similar analogy for plaque composition can beapplied to measures of vascular remodeling in a specific coronary lesionand/or across all coronary lesions within the coronary vascular tree. Inparticular, in some embodiments, the remodeling index can be acontinuous measure and can be reported by one or more of diameter, area,and/or volume. As positive remodeling can be associated with lesions atthe time of acute coronary syndrome and negative remodeling may not, insome embodiments, serial imaging (e.g., CT scans, etc.) can be followedacross time to determine whether the plaque is causing more or lesspositive remodeling. In some embodiments, cessation and/or slowing ofpositive remodeling can be favorable sign that can be used toprognostically update an individual or a lesion’s risk of myocardialinfarction or other adverse coronary event (e.g., ischemia, etc.).

In some embodiments, the system can be configured to provide a “treat tothe image” continuous approach that offers clinicians and patients amethod for following plaque changes over time to ensure that the plaqueis stabilizing and the prognosis is improving. For example, in someembodiments, a patient may be started on a statin medication after theirCT scan. Over time (e.g., months, etc.), a plaque may change inremodeling index from 1.10 to 1.08. In some embodiments, this mayrepresent a beneficial outcome of plaque stabilization and connote theefficacy of the statin medications on the plaque. Alternatively, overtime, a plaque may not change in remodeling index over time, staying at1.10. In this case, in some embodiments, this may represent an adverseoutcome wherein the statin medication is ineffective in stabilizing theplaque. In some embodiments, should a plaque not stabilize to medicaltherapy (for example if the remodeling index remains high or is veryslow to decrease), then another medication (e.g., PCSK-9 inhibitor,etc.) may be added, as the constancy in the remodeling can be atitratable biomarker that is used to guide medical therapyintensification and, ultimately, improve patient outcomes (e.g., byreducing myocardial infarction, rapid progression, ischemia, and/orother adverse event).

In some embodiments, remodeling indices of plaques may be influenced bya number of factors that can include one or more of: scanner type, imageacquisition parameters (e.g., mA, kVp, etc.), energy (e.g., single-,dual-, spectral, photon counting, etc.), gating (e.g., axial vs.retrospective helical, etc.), contrast, age, patient body habitus,surrounding cardiac structures, plaque type (e.g., calcium may causepartial volume artifact, etc.), and/or the like. In some embodiments,the system can be configured to normalize to one or more of thesefactors to further standardize comparisons in plaque types over time.

In some embodiments, the system can be configured to track location ofone or more regions of plaque over time. For example, in someembodiments, the system can be configured to track the location of oneor more regions of plaque based on one or more of: myocardial facing vs.pericardial facing; at a bifurcation or trifurcation; proximal vs. midvs. distal; main vessel vs. branch vessel; and/or the like. In someembodiments, the system can be configured to evaluate directionality ona patient, vessel, segment, lesion and/or cross section basis. In someembodiments, the system can be configured to leverage directionalitychanges to link to risk of disease events (e.g. more outward remodeling,higher risk of heart attack, and/or the like). In some embodiments, thesystem can be configured to guide decision making as to whether to addanother medication / intensify medical therapy or perform coronaryrevascularization, and/or the like. In some embodiments, the system canbe configured to guide decision making in the above manner in order toreduce adverse coronary events (e.g., acute coronary syndrome, rapidprogression, ischemia, and/or the like).

In some embodiments, the system can be configured to identify and/orcorrelate certain coronary events as being associated with increasedrisk over time. For example, in some embodiments, pericardial facingplaque may have a higher rate of being a culprit lesion at the time ofmyocardial infarction than myocardial facing plaques. In someembodiments, bifurcation lesions can appear to have a higher rate ofbeing a culprit lesion at the time of myocardial infarction thannon-bifurcation/trifurcation lesions. In some embodiments, proximallesions can tend to be more common than distal lesions and can also bemost frequently the site of myocardial infarction or other adversecoronary event.

In some embodiments, the system can be configured to track each or someone of these individual locations of plaque and, based upon theirpresence, extent and severity, assign a baseline risk. In someembodiments, after treatment with medication, lifestyle or intervention,serial imaging (e.g., by CT, etc.) can be performed to determine changesin these features, which can be used to update risk assessment.

In some embodiments, the system can be configured to provide a “treat tothe image” continuous approach that offers clinicians and patients amethod for following plaque changes in location over time to ensure thatthe plaque is stabilizing and the prognosis is improving. For example,in some embodiments, a patient may be started on a statin medicationafter their CT scan. Over time (e.g., months, etc.), a plaque mayregress in the pericardial-facing region but remain in the myocardialfacing region. In some embodiments, this may represent a beneficialoutcome of plaque stabilization and connote the efficacy of the statinmedications on the plaque. Alternatively, over time, a plaque may notchange in location over time and remain pericardial-facing. In thiscase, in some embodiments, this may represent an adverse outcome whereinthe statin medication is ineffective in stabilizing the plaque. In someembodiments, should a plaque not stabilize to medical therapy (forexample if the location of plaque remains pericardial-facing or is veryslow to change), then another medication (e.g., PCSK-9 inhibitor orother) may be added, as the constancy in the location of plaque can be atitratable biomarker that is used to guide medical therapyintensification and, ultimately, improve patient outcomes (e.g., byreducing myocardial infarction, rapid progression, ischemia, or otheradverse event).

In some embodiments, the CT appearance of plaque location may beinfluenced by a number of factors that may include one or more of:scanner type, image acquisition parameters (e.g., mA, kVp, etc.), energy(e.g., single-, dual-, spectral, photon counting, etc.), gating (e.g.,axial vs. retrospective helical, etc.), contrast, age, patient bodyhabitus, surrounding cardiac structures, plaque type (e.g., calcium maycause partial volume artifact, etc.), and/or others. In someembodiments, the system can be configured to normalize to one or more ofthese factors to further standardize comparisons in plaque types overtime.

In some embodiments, the system can be configured to track plaque volumeand/or plaque volume as a function of vessel volume (e.g., percentatheroma volume or PAV, etc.). In some embodiments, plaque volume and/orPAV can be tracked on a per-patient, per-vessel, per-segment orper-lesion basis. In some embodiments, the system can be configured toevaluate directionality of plaque volume or PAV (e.g., increasing,decreasing or staying the same). In some embodiments, the system can beconfigured to leverage directionality changes to link to risk of diseaseevents. For example, in some embodiments, an increase in plaque volumeor PAV can be indicative of higher risk. Similarly, in some embodiments,slowing of plaque progression can be indicative of lower risk and/or thelike. In some embodiments, the system can be configured to guidedecision making as to whether to add another medication / intensifymedical therapy or perform coronary revascularization. For example, insome embodiments, in response to increasing plaque volume or PAV, thesystem can be configured to propose increased / intensified medicaltherapy, other treatment, increased medication dosage, and/or the like.In some embodiments, the system can be configured to guide decisionmaking in order to reduce adverse coronary events (e.g., acute coronarysyndrome, rapid progression, ischemia, and/or the like).

In some embodiments, the system can be configured to identify and/orcorrelate certain adverse coronary events as being associated withincreased risk over time. For example, in some embodiments, higherplaque volume and/or higher PAV can result in high risk of CAD events.

In some embodiments, the system can be configured to track plaque volumeand/or PAV and assign a baseline risk based at least in part on itspresence, extent, and/or severity. In some embodiments, after treatmentwith medication, lifestyle or intervention, serial imaging (e.g., by CT)can be performed to determine changes in these features, which can beused to update risk assessment.

In some embodiments, the system can be configured to provide a “treat tothe image” continuous approach that offers clinicians and patients amethod for following plaque changes in location over time to ensure thatthe plaque is stabilizing and the prognosis is improving. For example,in some embodiments, in a patient may be started on a statin medicationafter their CT scan. Over time (e.g., months, etc.), a plaque mayincrease in volume or PAV. In some embodiments, this may represent anadverse outcome and connote the inefficacy of statin medications.Alternatively, over time, the volume of plaque may not change. In thiscase, in some embodiments, this may represent a beneficial outcomewherein the statin medication is effective in stabilizing the plaque. Insome embodiments, should a plaque not stabilize to medical therapy(e.g., if plaque volume or PAV increases), then another medication(e.g., PCSK-9 inhibitor and/or other) may be added, as the constancy inthe plaque volume or PAV can be a titratable biomarker that is used toguide medical therapy intensification and, ultimately, improve patientoutcomes (e.g., by reducing myocardial infarction, rapid progression,ischemia, and/or other adverse event).

In some embodiments, the CT appearance of plaque location may beinfluenced by a number of factors that may include one or more of:scanner type, image acquisition parameters (e.g., mA, kVp, etc.), energy(e.g., single-, dual-, spectral, photon counting, etc.), gating (e.g.,axial vs. retrospective helical, etc.), contrast, age, patient bodyhabitus, surrounding cardiac structures, plaque type (e.g., calcium maycause partial volume artifact, etc.), and/or others. In someembodiments, the system can be configured to normalize to one or more ofthese factors to further standardize comparisons in plaque types overtime.

In some embodiments, the system can be configured to analyze and/orreport one or more of the overall changes described above related toplaque composition, vascular remodeling, and/or other features on aper-patient, per-vessel, per-segment, and/or per-lesion basis, forexample to provide prognostic risk stratification either in isolation(e.g., just composition, etc.) and/or in combination (e.g.,composition + remodeling + location, etc.).

In some embodiments, the system can be configured to update riskassessment and/or guide medical therapy, lifestyle changes, and/orinterventional therapy based on image analysis and/or disease tracking.In particular, in some embodiments, the system can be configured toreport in a number of ways changes to arteries/plaques that occur on acontinuous basis as a method for tracking disease stabilization orworsening. In some embodiments, as a method of tracking disease, thesystem can be configured to report the risk of adverse coronary events.For example, in some embodiments, based upon imaging-based changes, aquantitative risk score can be updated from baseline at follow-up. Insome embodiments, the system can be configured to utilize a 4-categorymethod that analyzes: (1) progression - entails worsening (e.g., lowerattenuation, greater positive remodeling, etc.); (2) regression -entails diminution (e.g., higher attenuation, lower positive remodeling,etc.); (3) mixed response - progression, but of more prognosticallybeneficial findings (e.g., higher volume of plaque over time, but withcalcified 1K plaque dominant) (mixed response can also include plaqueremodeling and location); and/or (4) mixed response - progression, butof more prognostically adverse findings (higher volume of plaque overtime, but with more non-calcified low attenuation plaques) (mixedresponse can also include plaque remodeling and location). In someembodiments, for tracking disease as a method to guide therapy,intensification of medical therapy and/or institution of lifestylechanges or coronary revascularization may occur and be prompted byincreased risk of adverse coronary events or being in the “progression”or “mixed response - progression of calcified plaque” categories forexample. Further, in some embodiments, serial tracking of disease andappropriate intensification of medical therapy, lifestyle changes orcoronary revascularization based upon composition, remodeling and/orlocation changes, can be provided as a guide to reduce adverse coronaryevents.

FIG. 22B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for treating to the image. As illustrated inFIG. 22B, in some embodiments, the system is configured to access afirst set of plaque and/or vascular parameters of a subject, such as forexample relating to the coronaries, at block 2202. In some embodiments,one or more plaque and/or vascular parameters can be accessed from aplaque and/or vascular parameter database 2204. In some embodiments, oneor more plaque and/or vascular parameters can be derived and/or analyzedfrom one or more medical images being stored in a medical image database100.

The one or more plaque parameters and/or vascular parameters can includeany such parameters described herein. As a non-limiting example, the oneor more plaque parameters can include one or more of density, location,or volume of one or more regions of plaque. The density can be absolutedensity, Hounsfield unit density, and/or the like. The location of oneor more regions of plaque can be determined as one or more of myocardialfacing, pericardial facing, at a bifurcation, at a trifurcation,proximal, mid, or distal along a vessel, or in a main vessel or branchvessel, and/or the like. The volume can be absolute volume, PAV, and/orthe like. Further, the one or more vascular parameters can includevascular remodeling or any other vascular parameter described herein.For example, vascular remodeling can include directionality changes inremodeling, such as outward, intermediate, or inward. In someembodiments, vascular remodeling can include vascular remodeling of oneor more coronary atherosclerotic lesions.

In some embodiments, at block 2206, the subject can be treated with somemedical treatment to address a disease, such as CAD. In someembodiments, the treatment can include one or more medications,lifestyle changes or conditions, revascularization procedures, and/orthe like. For example, in some embodiments, medication can includestatins, human immunodeficiency virus (HIV) medications, icosapentethyl, bempedoic acid, rivaroxaban, aspirin, proprotein convertasesubtilisin/kexin type 9 (PCSK-9) inhibitors, inclisiran, sodium-glucosecotransporter-2 (SGLT-2) inhibitors, glucagon-like peptide-1 (GLP-1)receptor agonists, low-density lipoprotein (LDL) apheresis, and/or thelike. In some embodiments, lifestyle changes or condition can includeincreased exercise, aerobic exercise, anaerobic exercise, cessation ofsmoking, change in diet, and/or the like. In some embodiments,revascularization can include bypass grafting, stenting, use of abioabsorbable scaffold, and/or the like.

In some embodiments, at block 2208, the system can be configured toaccess one or more medical images of the subject taken after the subjectis treated with the medical treatment for some time. The medical imagecan include any type of image described herein, such as for example, CT,MRI, and/or the like. In some embodiments, at block 2210, the system canbe configured to identify one or more regions of plaque on the one ormore medical images, for example using one or more image analysistechniques described herein. In some embodiments, at block 2212, thesystem can be configured to analyze the one or more medical images todetermine a second set of plaque and/or vascular parameters. The secondset of plaque and/or vascular parameters can be stored and/or accessedfrom the plaque and/or vascular parameter database 2204 in someembodiments. The second set of plaque and/or vascular parameters caninclude any parameters described herein, including for example those ofthe first set of plaque and/or vascular parameters.

In some embodiments, the system at block 2214 can be configured tonormalize one or more of the first set of plaque parameters, first setof vascular parameters, second set of plaque parameters, and/or secondset of vascular parameters. As discussed herein, one or more suchparameters or quantification thereof can depend on the scanner type orscan parameter used to obtain a medical image from which such parameterswere derived from. As such, in some embodiments, it can be advantageousto normalize for such differences. To do so, in some embodiments, thesystem can be configured to utilize readings obtained from anormalization device as described herein.

In some embodiments, the system at block 2216 can be configured toanalyze one or more changes between the first set of plaque parametersand the second set of plaque parameters. For example, in someembodiments, the system can be configured to analyze changes between aspecific type of plaque parameter. In some embodiments, the system canbe configured to generate a first weighted measure of one or more of thefirst set of plaque parameters and a second weighted measure of one ormore of the second set of plaque parameters and analyze changes betweenthe first weighted measure and the second weighted measure. The weightedmeasure can be generated in some embodiments by applying a mathematicaltransform or any other technique described herein.

In some embodiments, the system at block 2218 can be configured toanalyze one or more changes between the first set of vascular parametersand the second set of vascular parameters. For example, in someembodiments, the system can be configured to analyze changes between aspecific type of vascular parameter. In some embodiments, the system canbe configured to generate a first weighted measure of one or more of thefirst set of vascular parameters and a second weighted measure of one ormore of the second set of vascular parameters and analyze changesbetween the first weighted measure and the second weighted measure. Theweighted measure can be generated in some embodiments by applying amathematical transform or any other technique described herein.

In some embodiments, at block 2220, the system can be configured totrack the progression of a disease, such as CAD, based on the analyzedchanges between one or more plaque parameters and/or vascularparameters. In some embodiments, the system can be configured todetermine progression of a disease based on analyzing changes between aweighted measure of one or more plaque parameters and/or vascularparameters as described herein. In some embodiments, the system can beconfigured to determine progression of a disease based on analyzingchanges between one or more specific plaque parameters and/or vascularparameters. In particular, in some embodiments, an increase in densityof the one or more regions of plaque can be indicative of diseasestabilization. In some embodiments, a change in location of a region ofplaque from pericardial facing to myocardial facing is indicative ofdisease stabilization. In some embodiments, an increase in volume of theone or more regions of plaque between the first point in time and thesecond point in time is indicative of disease stabilization. In someembodiments, more outward remodeling between the first point in time andthe second point in time is indicative of disease stabilization. In someembodiments, disease progression is tracked on one or more of aper-subject, per-vessel, per-segment, or per-lesion basis. In someembodiments, disease progression can be determined by the system as oneor more of progression, regression, mixed response — progression ofcalcified plaque, mixed response — progression of non-calcified plaque.

In some embodiments, at block 2222, the system can be configured todetermine the efficacy of the medical treatment, for example based onthe tracked disease progression. As such, in some embodiments, changesin one or more plaque and/or vascular parameters as derived from one ormore medical images using image analysis techniques can be used as abiomarker for assessing treatment. In some embodiments, the system canbe configured to determine efficacy of a treatment based on analyzingchanges between a weighted measure of one or more plaque parametersand/or vascular parameters as described herein. In some embodiments, thesystem can be configured to determine efficacy of a treatment based onanalyzing changes between one or more specific plaque parameters and/orvascular parameters. In particular, in some embodiments, an increase indensity of the one or more regions of plaque can be indicative of apositive efficacy of the medical treatment. In some embodiments, achange in location of a region of plaque from pericardial facing tomyocardial facing is indicative of a positive efficacy of the medicaltreatment. In some embodiments, an increase in volume of the one or moreregions of plaque between the first point in time and the second pointin time is indicative of a negative efficacy of the medical treatment.In some embodiments, more outward remodeling between the first point intime and the second point in time is indicative of a negative efficacyof the medical treatment.

In some embodiments, at block 2224, the system is configured to generatea proposed medical treatment for the subject based on the determinedefficacy of the prior treatment. For example, if the prior treatment isdetermined to be positive or stabilizing the disease, the system can beconfigured to propose the same treatment. In some embodiments, if theprior treatment is determined to be negative or not stabilizing thedisease, the system can be configured to propose a different treatment.The newly proposed treatment can include any of the types of treatmentdiscussed herein, for example including those discussed in connectionwith the prior treatment at block 2206.

Determining Treatment(s) for Reducing Cardiovascular Risk and/or Events

Some embodiments of the systems, devices, and methods described hereinare configured to determine a treatment(s) for reducing cardiovascularrisk and/or events. In particular, some embodiments of the systems andmethods described herein are configured to automatically and/ordynamically determine or generate lifestyle, medication and/orinterventional therapies based upon actual atheroscleroticcardiovascular disease (ASCVD) burden, ASCVD type, and/or and ASCVDprogression. As such, some systems and methods described herein canprovide personalized medical therapy is based upon CCTA-characterizedASCVD. In some embodiments, the systems and methods described herein areconfigured to dynamically and/or automatically analyze medical imagedata, such as for example non-invasive CT, MRI, and/or other medicalimaging data of the coronary region of a patient, to generate one ormore measurements indicative or associated with the actual ASCVD burden,ASCVD type, and/or ASCVD progression, for example using one or moreartificial intelligence (AI) and/or machine learning (ML) algorithms. Insome embodiments, the systems and methods described herein can furtherbe configured to automatically and/or dynamically generate one or morepatient-specific treatments and/or medications based on the actual ASCVDburden, ASCVD type, and/or ASCVD progression, for example using one ormore artificial intelligence (AI) and/or machine learning (ML)algorithms. In some embodiments, the system can be configured to utilizea normalization device, such as those described herein, to account fordifferences in scan results (such as for example density values, etc.)between different scanners, scan parameters, and/or the like.

In some embodiments of cardiovascular risk assessment of asymptomaticindividuals, the system can be configured to use one or more riskfactors to guide risk stratification and treatment. For example, somecardiovascular risk factors can include measurements of surrogatemeasures of coronary artery disease (CAD) of clinical states thatcontribute to CAD, including dyslipidemia, hypertension, diabetes,and/or the like. In some embodiments, such factors can form the basis oftreatment recommendations in professional societal guidelines, which canhave defined goals for medical treatment and lifestyle based upon thesesurrogate markers of CAD, such as total and LDL cholesterol (bloodbiomarkers), blood pressure (biometric) and hemoglobin A1C (bloodbiomarker). In some embodiments, this approach can improvepopulation-based survival and reduces the incidence of heart attacks andstrokes. However, in some embodiments, these methods also suffer a lackof specificity, wherein treatment can be more effective in populationsbut may not pinpoint individual persons who harbor residual risk. As anexample, LDL has been found in population-based studies to explain only29% of future heart attacks and, even in the pivotal statin treatmenttrials, those individuals treated effectively with statins still retain70-75% residual risk of heart attacks.

As such, some embodiments described herein address such technicalshortcomings by leveraging lifestyle, medication and/or interventionaltherapies based upon actual atherosclerotic cardiovascular disease(ASCVD) burden, ASCVD type, and/or and ASCVD progression. Given themultitude of medications available to target the ASCVD process throughatherosclerosis, thrombosis and inflammatory pathways, in someembodiments, such direct precision-medicine ASCVD diagnosis andtreatment approach can be more effective than treating surrogate markersof ASCVD at the individual level.

In some embodiments, the systems and methods described herein areconfigured to automatically and/or dynamically determine or generatelifestyle, medication and/or interventional therapies based upon actualatherosclerotic cardiovascular disease (ASCVD) burden, ASCVD type,and/or and ASCVD progression. In particular, in some embodiments, thesystems and methods are configured to use coronary computed tomographicangiography (CCTA) for quantitative assessment of ASCVD in one or moreor all vascular territories, including for example coronary, carotid,aortic, lower extremity, cerebral, renal arteries, and/or the like. Insome embodiments, the systems and methods are configured to analyzeand/or utilize not only the amount (or burden) of ASCVD, but also thetype of plaque in risk stratification. For example, in some embodiments,the systems and methods are configured to associate low attenuationplaques (LAP) and/or non-calcified plaques (NCP) of certain densitieswith future major adverse cardiovascular events (MACE), whilstassociating calcified plaques and, in particular, calcified plaques ofhigher density as being more stable. Further, in some embodiments, thesystems and methods are configured to generate a patient-specifictreatment plan that can include use of medication that has beenassociated with a reduction in LAP or NCP of certain densities and/or anacceleration in calcified plaque formation in populations, i.e., atransformation of plaque by compositional burden. In some embodiments,the systems and methods are configured to generate a patient-specifictreatment plan that can include use of medications which can be observedby CCTA to be associated with modification of ASCVD in the coronaryarteries, carotid arteries, and/or other arteries, such as for examplestatins, PCSK9 inhibitors, GLP receptor agonists, icosapent ethyl,and/or colchicine, amongst others.

As described herein, in some embodiments, the systems and methods areconfigured to leverage ASCVD burden, type, and/or progression tologically guide clinical decision making. In particular, in someembodiments, the systems and methods described herein are configured toleverage, analyze, and/or utilize ASCVD burden, type, and/or progressionto guide medical therapy to reduce adverse ASCVD events and/or improvepatient-specific event-free survival in a personalized fashion. Forexample, in some embodiments, the system can be configured to analyzeand/or utilize ASCVD type, such as peri-lesion tissue atmosphere,localization, and/or the like.

More specifically, in some embodiments, the systems and methodsdescribed herein are configured to utilize one or more CCTA algorithmsand/or one or more medical treatment algorithms that quantify thepresence, extent, severity and/or type of ASCVD, such as for example itslocalization and/or peri-lesion tissues. In some embodiments, the one ormore medical treatment algorithms are configured to analyze any medicalimages obtained from any imaging modality, such as for example computedtomography (CT), magnetic resonance (MR), ultrasound, nuclear medicine,molecular imaging, and/or others. In some embodiments, the systems andmethods described herein are configured to utilize one or more medicaltreatment algorithms that are personalized (rather thanpopulation-based), treat actual disease (rather than surrogate markersof disease, such as risk factors), and/or are guided by changes inCCTA-identified ASCVD over time (such as for example, progression,regression, transformation, and/or stabilization). In some embodiments,the one or more CCTA algorithms and/or the one or more medical treatmentalgorithms are computer-implemented algorithms and/or utilize one ormore AI and/or ML algorithms.

In some embodiments, the systems and methods are configured to assess abaseline ASCVD in an individual. In some embodiments, the systems andmethods are configured to evaluate ASCVD by utilizing coronary CTangiography (CCTA). In some embodiments, the systems and methods areconfigured to identify and/or analyze the presence, local, extent,severity, type of atherosclerosis, peri-lesion tissue characteristics,and/or the like. In some embodiments, the method of ASCVD evaluation canbe dependent upon quantitative imaging algorithms that perform analysisof coronary, carotid, and/or other vascular beds (such as, for example,lower extremity, aorta, renal, and/or the like).

In some embodiments, the systems and methods are configured tocategorize ASCVD into specific categories based upon risk. For example,some example of such categories can include: Stage 0, Stage I, Stage II,Stage III; or none, minimal, mild, moderate/severe; or primarilycalcified vs. primarily non-calcified; or X units of low densitynon-calcified plaque); or X% of NCP as a function of overall volume orburden. In some embodiments, the systems and methods can be configuredto quantify ASCVD continuously. In some embodiments, the systems andmethods can be configured to define categories by levels of future ASCVDrisk of events, such as heart attack, stroke, amputation, dissection,and/or the like. In some embodiments, one or more other non-ASCVDmeasures may be included to enhance risk assessment, such as for examplecardiovascular measurements (e.g., left ventricular hypertrophy forhypertension; atrial volumes for atrial fibrillation; fat; etc.) and/ornon-cardiovascular measurements that may contribute to ASCVD (e.g.,emphysema, etc.). In some embodiments, these measurements can bequantified using one or more CCTA algorithms.

In some embodiments, the systems and methods described herein can beconfigured to generate a personalized or patient-specific treatment.More specifically, in some embodiments, the systems and methods can beconfigured to generate therapeutic recommendations based upon ASCVDpresence, extent, severity, and/or type. In some embodiments, ratherthan utilizing risk factors (such as, for example, cholesterol,diabetes), the treatment algorithm can comprise and/or utilize a tieredapproach that intensifies medical therapy, lifestyle, and/orinterventional therapies based upon ASCVD directly in a personalizedfashion. In some embodiments, the treatment algorithm can be configuredto generally ignore one or more conventional markers of success (e.g.,lowering cholesterol, hemoglobin A1C, etc.) and instead leverage ASCVDpresence, extent, severity, and/or type of disease to guide therapeuticdecisions of medical therapy intensification. In some embodiments, thetreatment algorithm can be configured to combine one or moreconventional markers of success (e.g., lowering cholesterol, hemoglobinA1C, etc.) with ASCVD presence, extent, severity, and/or type of diseaseto guide therapeutic decisions of medical therapy intensification. Insome embodiments, the treatment algorithm can be configured to combineone or more novel markers of success (e.g., such as genetics,transcriptomics, or other ‘omics measurements, etc.) with ASCVDpresence, extent, severity, and/or type of disease to guide therapeuticdecisions of medical therapy intensification. In some embodiments, thetreatment algorithm can be configured to combine one or more otherimaging markers of success (e.g., such as carotid ultrasound imaging,abdominal aortic ultrasound or computed tomography, lower extremityarterial evaluation, and/or others) with ASCVD presence, extent,severity, and/or type of disease to guide therapeutic decisions ofmedical therapy intensification.

In some embodiments, the systems and methods are configured to perform aresponse assessment. In particular, in some embodiments, the systems andmethods are configured to perform repeat and/or serial CCTA in order todetermine the efficacy of therapy on a personalized basis, and todetermine progression, stabilization, transformation, and/or regressionof ASCVD. In some embodiments, progression can be defined as rapid ornon-rapid. In some embodiments, stabilization can be defined astransformation of ASCVD from non-calcified to calcified, or reduction oflow attenuation plaque, or reduction of positive arterial remodeling. Insome embodiments, regression of ASCVD can be defined as a decrease inASCVD volume or burden or a decrease in specific plaque types, such asnon-calcified or low attenuation plaque.

In some embodiments, the systems and methods are configured to updatepersonalized treatment based upon response assessment. In particular, insome embodiments, based upon the change in ASCVD between the baselineand follow-up CCTA, personalized treatment can be updated andintensified if worsening occurs or de-escalated / kept constant ifimprovement occurs. As a non-limiting example, if stabilization hasoccurred, this can be evidence of the success of the current medicalregimen. Alternatively, as another non-limiting example, ifstabilization has not occurred and ASCVD has progressed, this can beevidence of the failure of the current medical regimen, and analgorithmic approach can be taken to intensify medical therapy.

In some embodiments, the intensification regimen employs lipid loweringagents in a tiered fashion, and considers ASCVD presence, extent,severity, type, and/or progression. In some embodiments, theintensification regimen considers local and/or peri-lesion tissue. Insome embodiments, the intensification regimen and use of the medicationstherein can be guided also by LDL cholesterol and triglyceride (TG) andLp(a) and Apo(B) levels; or cholesterol particle density and size. Forexample, FIGS. 23F-G illustrate an example embodiment(s) of atreatment(s) employing lipid lowering medication(s) and/or treatment(s)generated by an example embodiment(s) of systems and methods fordetermining treatments for reducing cardiovascular risk and/or events.

In some embodiments, given the multidimensional nature of MACEcontributors that include ASCVD, inflammation and thrombosis, theintensification regimen can incorporate anti-inflammatory medications(e.g., colchicine) and/or anti-thrombotic medications (e.g., rivaroxabanand aspirin) in order to control the ASCVD progress. In someembodiments, new diabetic medications that have salient effects onreducing MACE events— including SGLT2 inhibitors and GLP1R agonists—canalso be incorporated. For example, FIGS. 23H-I illustrate an exampleembodiment(s) of a treatment(s) employing diabetic medication(s) and/ortreatment(s) generated by an example embodiment(s) of systems andmethods for determining treatments for reducing cardiovascular riskand/or events.

FIG. 23A illustrates an example embodiment(s) of systems and methods fordetermining treatments for reducing cardiovascular risk and/or events.In some embodiments, the systems and methods described herein areconfigured to analyze coronaries. In some embodiments, the systems andmethods can also be applied to other arterial bed as well, such as theaorta, carotid, lower extremity, renal artery, cerebral artery, and/orthe like.

In some embodiments, the system can be configured to determine and/orutilize in its analysis the presence of ASCVD, which can be the presencevs. absence of plaque, the presence vs. absence of non-calcified plaque,the presence vs. absence of low attenuation plaque, and/or the like.

In some embodiments, the system can be configured to determine and/orutilize in its analysis the extent of ASCVD, which can include the totalASCVD volume, percent atheroma volume (atheroma volume / vessel volume ×100), total atheroma volume normalized to vessel length (TAVnorm),diffuseness (% of vessel affected by ASCVD), and/or the like.

In some embodiments, the system can be configured to determine and/orutilize in its analysis severity of ASCVD. In some embodiments, ASCVDseverity can be linked to population-based estimates normalized to age-,gender-, ethnicity-, CAD risk factors, and/or the like. In someembodiments, ASCVD severity can include angiographic stenosis >70%or >50% in none, 1-, 2-, and/or 3-VD.

In some embodiments, the system can be configured to determine and/orutilize in its analysis the type of ASCVD, which can include for examplethe proportion (ratio, %, etc.) of plaque that is non-calcified vs.calcified, proportion of plaque that is low attenuation non-calcifiedvs. non-calcified vs. low density calcified vs. high-density calcified,absolute amount of non-calcified plaque and calcified plaque, absoluteamount of plaque that is low attenuation non-calcified vs. non-calcifiedvs. low density calcified vs. high-density calcified, continuousgrey-scale measurement of plaques without ordinal classification,radiomic features of plaque, including heterogeneity and others,vascular remodeling imposed by plaque as positive remodeling (>1.10or >1.05 ratio of vessel diameter / normal reference diameter; or vesselarea / normal reference area; or vessel volume / normal referencevolume) vs. negative remodeling (<1.10 or <1.05), vascular remodelingimposed by plaque as a continuous ratio, and/or the like.

In some embodiments, the system can be configured to determine and/orutilize in its analysis the locality of plaque, such as for example inthe arterial bed, regarding vessel, segment, bifurcation, and/or thelike.

In some embodiments, the system can be configured to determine and/orutilize in its analysis the peri-lesion tissue environment, such as forexample density of the peri-plaque tissues such as fat, amount of fat inthe peri-vascular space, radiomic features of peri-lesion tissue,including heterogeneity and others, and/or the like.

In some embodiments, the system can be configured to determine and/orutilize in its analysis ASCVD progression. In some embodiments,progression can be defined as rapid vs. non-rapid, with thresholds todefine rapid progression (e.g., >1.0% percent atheroma volume, >200 mm3plaque, etc.). In some embodiments, serial changes in ASCVD can includerapid progression, progression with primarily calcified plaqueformation, progression with primarily non-calcified plaque formation,and regression.

In some embodiments, the system can be configured to determine and/orutilize in its analysis one or more categories of risk. In someembodiments, the system can be configured to utilize one or more stages,such as 0, I, II, or III based upon plaque volumes associated withangiographic severity (such as, for example, none, non-obstructive, andobstructive 1VD, 2VD and 3VD). In some embodiments, the system can beconfigured to utilize one or more percentiles, for example taking intoaccount age, gender, ethnicity, and/or presence of one or more riskfactors (such as, diabetes, hypertension, etc.). In some embodiments,the system can be configured to determine a percentage of calcifiedplaque vs. percentage of non-calcified plaque as a function of overallplaque volume. In some embodiments, the system can be configured todetermine the number of units of low density non-calcified plaque. Insome embodiments, the system can be configured to generate a continuous3D histogram and/or geospatial map (for plaque geometry) analysis ofgrey scales of plaque by lesion, by vessel, and/or by patient. In someembodiments, risk can be defined in a number of ways, including forexample risk of MACE, risk of angina, risk of ischemia, risk of rapidprogression, risk of medication non-response, and/or the like.

In some embodiments, treatment recommendations can be based upon ASCVDpresence, extent, severity type of disease, ASCVD progression, and/orthe like. For example, FIGS. 23F-G illustrate an example embodiment(s)of a treatment(s) employing lipid lowering medication(s) and/ortreatment(s) and FIGS. 23H-I illustrate an example embodiment(s) of atreatment(s) employing diabetic medication(s) and/or treatment(s)generated by an example embodiment(s) of systems and methods fordetermining treatments for reducing cardiovascular risk and/or events.

In some embodiments, the generated treatment protocols are aimed (e.g.,based upon CCTA-based ASCVD characterization) to properly treat at theright point in time with medications aimed at ASCVD stabilization,inflammation reduction, and/or reduction of thrombosis potential. Insome embodiments, the rationale behind this is that ASCVD events can bean inflammatory atherothrombotic phenomenon, but serum biomarkers,biometrics and conventional measures of angiographic stenosis severitycan be inadequate to optimally define risk and guidance to clinicaldecision making. As such, some systems and methods described herein canprovide personalized medical therapy is based upon CCTA-characterizedASCVD.

In some embodiments, the system can be configured to generate a riskscore that combines one or more traditional risk factors, such as theones described herein, together with one or more quantified ASCVDmeasures. In some embodiments, the system can be configured to generatea risk score that combines one or more genetics analysis with one ormore quantified ASCVD measures, as some medications may work better onsome people and/or people with particular genes. In addition, in someembodiments, the system can be configured to exclude or deduct certainplaque from the rest of disease. For example, in some embodiments, thesystem can be configured to ignore or exclude high density calcium thatis so stable that the risk of having it can be better than having adisease without it, such that the existence of such plaque may impactrisk negatively.

FIGS. 23B-C illustrate an example embodiment(s) of definitions orcategories of atherosclerosis severity used by an example embodiment(s)of systems and methods for determining treatments for reducingcardiovascular risk and/or events.

FIG. 23D illustrates an example embodiment(s) of definitions orcategories of disease progression, stabilization, and/or regression usedby an example embodiment(s) of systems and methods for determiningtreatments for reducing cardiovascular risk and/or events.

FIG. 23E illustrates an example embodiment(s) of a time-to-treatmentgoal(s) for an example embodiment(s) of systems and methods fordetermining treatments for reducing cardiovascular risk and/or events.

FIG. 23J is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determining treatments for reducingcardiovascular risk and/or events. As illustrated in FIG. 23J, in someembodiments, the system is configured to determine a proposedpersonalized treatment for a subject to lower ASCVD risk based on CCTAanalysis using one or more quantitative image analysis techniques and/oralgorithms.

In particular, in some embodiments, the system can be configured toaccess one or more medical images taken from a first point in time atblock 2302, for example from a medical image database 100. The one ormore medical images can include images obtained using any imagingmodality described herein. In some embodiments, the one or more medicalimages can include one or more arteries, such as for example coronary,carotid, lower extremity, upper extremity, aorta, renal, and/or thelike.

In some embodiments, the system at block 2304 can be configured toanalyze the one or more medical images. More specifically, in someembodiments, the system can be configured to utilize CCTA analysisand/or quantitative imaging algorithms to identify and/or derive one ormore parameters from the medical image. In some embodiments, the systemcan be configured to store one or more identified and/or derivedparameters in a parameter database 2306. In some embodiments, the systemcan be configured to access one or more such parameters from a parameterdatabase 2306. In some embodiments, the system can be configured toanalyze one or more plaque parameters, vascular parameters,atherosclerosis parameters, and/or perilesional tissue parameters. Theplaque parameters and/or vascular parameters can include any one or moresuch parameters discussed herein.

In some embodiments, at block 2308, the system can be configured toassess a baseline ASCVD risk of the subject based on one or more suchparameters. In some embodiments, at block 2310, the system can beconfigured to categorize the baseline ASCVD risk of the subject. In someembodiments, the system can be configured to categorize the baselineASCVD risk into one or more predetermined categories. For example, insome embodiments, the system can be configured to categorize thebaseline ASCVD risk as one of Stage 0, I, II, or III. In someembodiments, the system can be configured to categorize the baselineASCVD risk as one of none, minimal, mild, or moderate. In someembodiments, the system can be configured to categorize the baselineASCVD risk as one of primarily calcified or primarily non-calcifiedplaque. In some embodiments, the system can be configured to categorizethe baseline ASCVD risk based on units of low density non-calcifiedplaque identified from the image. In some embodiments, the system isconfigured to categorize the baseline ASCVD risk on a continuous scale.In some embodiments, the system is configured to categorize the baselineASCVD risk based on risk of future ASCVD events, such as heart attack,stroke, amputation, dissection, and/or the like. In some embodiments,the system is configured to categorize the baseline ASCVD risk based onone or more non-ASCVD measures, which can be quantified using one ormore CCTA algorithms. For example, non-ASCVD measures can include one ormore cardiovascular measurements (e.g., left ventricular hypertrophy forhypertension or atrial volumes for atrial fibrillation, and/or the like)or non-cardiovascular measurements that may contribute to ASCVD (e.g.,emphysema, etc.).

In some embodiments, the system at block 2312 can be configured todetermine an initial proposed treatment for the subject. In someembodiments, the system can be configured to determine an initialproposed treatment with or without analysis of cholesterol or hemoglobinA1C. In some embodiments, the system can be configured to determine aninitial proposed treatment with or without analysis of low-densitylipoprotein (LDL) cholesterol or triglyceride (TG) levels of thesubject.

In some embodiments, the initial proposed treatment can include medicaltherapy, lifestyle therapy, and/or interventional therapy. For example,medical therapy can include one or more medications, such aslipid-lowering medications, anti-inflammatory medications (e.g.,colchicine, etc.), anti-thrombotic medications (e.g., rivaroxaban,aspirin, etc.), diabetic medications (e.g., sodium-glucosecotransporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 receptor(GLP1R) agonists, etc.), and/or the like. Lifestyle therapy and/orinterventional therapy can include any one or more such therapiesdiscussed herein. In some embodiments, at block 2314, the subject can betreated with one or more such medical treatments.

In some embodiments, the system at block 2316 can be configured toaccess one or more medical images taken from a second point in timeafter the subject is treated with the initial treatment, for examplefrom a medical image database 100. The one or more medical images caninclude images obtained using any imaging modality described herein. Insome embodiments, the one or more medical images can include one or morearteries, such as for example coronary, carotid, lower extremity, upperextremity, aorta, renal, and/or the like.

In some embodiments, the system at block 2318 can be configured toanalyze the one or more medical images taken at the second point intime. More specifically, in some embodiments, the system can beconfigured to utilize CCTA analysis and/or quantitative imagingalgorithms to identify and/or derive one or more parameters from themedical image. In some embodiments, the system can be configured tostore one or more identified and/or derived parameters in a parameterdatabase 2306. In some embodiments, the system can be configured toaccess one or more such parameters from a parameter database 2306. Insome embodiments, the system can be configured to analyze one or moreplaque parameters, vascular parameters, atherosclerosis parameters,and/or perilesional tissue parameters. The plaque parameters and/orvascular parameters can include any one or more such parametersdiscussed herein.

In some embodiments, at block 2320, the system can be configured toassess an updated ASCVD risk of the subject based on one or more suchparameters. In some embodiments, at block 2322, the system can beconfigured to categorize the updated ASCVD risk of the subject. In someembodiments, the system can be configured to categorize the updatedASCVD risk into one or more predetermined categories. For example, insome embodiments, the system can be configured to categorize the updatedASCVD risk as one of Stage 0, I, II, or III. In some embodiments, thesystem can be configured to categorize the updated ASCVD risk as one ofnone, minimal, mild, or moderate. In some embodiments, the system can beconfigured to categorize the updated ASCVD risk as one of primarilycalcified or primarily non-calcified plaque. In some embodiments, thesystem can be configured to categorize the updated ASCVD risk based onunits of low density non-calcified plaque identified from the image. Insome embodiments, the system is configured to categorize the updatedASCVD risk on a continuous scale. In some embodiments, the system isconfigured to categorize the updated ASCVD risk based on risk of futureASCVD events, such as heart attack, stroke, amputation, dissection,and/or the like. In some embodiments, the system is configured tocategorize the updated ASCVD risk based on one or more non-ASCVDmeasures, which can be quantified using one or more CCTA algorithms. Forexample, non-ASCVD measures can include one or more cardiovascularmeasurements (e.g., left ventricular hypertrophy for hypertension oratrial volumes for atrial fibrillation, and/or the like) ornon-cardiovascular measurements that may contribute to ASCVD (e.g.,emphysema, etc.).

In some embodiments, the system at block 2324 can be configured toassess the subject’s response to the initial proposed treatment. Forexample, in some embodiments, the system can be configured to comparedifferences or changes in ASCVD risk and/or categorized ASCVD riskbetween the first point in time and the second point in time. In someembodiments, the subject response is assessed based on one or more ofprogression, stabilization, or regression of ASCVD. In some embodiments,progression can include rapid and/or non-rapid progression. In someembodiments, stabilization can include transformation of ASCVD fromnon-calcified to calcified, reduction of low attenuation plaque, and/orreduction of positive arterial remodeling. In some embodiments,regression can include decrease in ASCVD volume or burden, decrease innon-calcified plaque, and/or decrease in low attenuation plaque.

In some embodiments, the system at block 2326 can be configured todetermine a continued proposed treatment for the subject, for examplebased on the subject response to the initial treatment. In particular,in some embodiments, if the system determines that there was progressionin ASCVD risk in response to the initial treatment, the system can beconfigured to propose a higher tiered treatment compared to the initialtreatment. In some embodiments, if the system determines that there wasstabilization or regression in ASCVD risk in response to the initialtreatment, the system can be configured to propose the same initialtreatment or a same or similar tiered alternative treatment or a lowertiered treatment compared to the initial treatment. In some embodiments,the system can be configured to determine a continued proposed treatmentwith or without analysis of cholesterol or hemoglobin A1C. In someembodiments, the system can be configured to determine a continuedproposed treatment with or without analysis of low-density lipoprotein(LDL) cholesterol or triglyceride (TG) levels of the subject.

In some embodiments, the continued proposed treatment can includemedical therapy, lifestyle therapy, and/or interventional therapy. Forexample, medical therapy can include one or more medications, such aslipid-lowering medications, anti-inflammatory medications (e.g.,colchicine, etc.), anti-thrombotic medications (e.g., rivaroxaban,aspirin, etc.), diabetic medications (e.g., sodium-glucosecotransporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 receptor(GLP1R) agonists, etc.), and/or the like. Lifestyle therapy and/orinterventional therapy can include any one or more such therapiesdiscussed herein.

In some embodiments, the system can be configured to repeat one or moreprocesses described in connection with FIG. 23J at different points intime. In other words, in some embodiments, the system can be configuredto apply serial analysis and/or tracking of treatments to continue tomonitor ASCVD of a subject and the subject’s response to treatment forcontinued treatment of the subject.

Determining Treatment(s) for Reducing Cardiovascular Risk and/or Events

Some embodiments of the systems, devices, and methods described hereinare configured to determine stenosis severity and/or vascular remodelingin the presence of atherosclerosis. In particular, some embodiments ofthe systems, devices, and methods described herein are configured todetermine stenosis severity and vascular remodeling, for example whilstaccounting for presence of plaque, natural artery tapering, and/or 3Dvolumes. In some embodiments, the systems, devices, and methodsdescribed herein are configured to determine % fractional blood volume,for example for determining of contribution of specific arteries and/orbranches to important pathophysiologic processes (such as, risk of sizeof myocardial infarction; ischemia, and/or the like), whilst accountingfor the presence of plaque in non-normal arteries. In some embodiments,the systems, methods, and devices described herein are configured todetermine ischemia, for example by applying the continuity equation,whilst accounting for blood flow across a range of physiologicallyrealistic ranges (e.g., ranges for rest, mild/moderate/extreme exercise,and/or the like).

Generally speaking, coronary artery imaging can be a key component fordiagnosis, prognostication and/or clinical decision making of patientswith suspected or known coronary artery disease (CAD). Morespecifically, in some embodiments, an array of coronary artery imagingparameters can be useful for guiding and informing these clinical tasksand can include such measures of arterial narrowing (stenosis) andvascular remodeling.

In some embodiments, the system can be configured to define relativearterial narrowing (stenosis) due to coronary artery atheroscleroticlesions. In some embodiments, these measures can largely rely upon (1)comparisons to diseased regions to normal regions of coronary vessels,and/or (2) 2D measures of diameter or area reduction due to coronaryartery lesions. However, limitations can exist in such embodiments.

For example, in some of such embodiments, relative narrowing can bedifficult to determine in diseased vessels. Specifically, in someembodiments, coronary stenosis can be reported as a relative narrowing,i.e., Diameter disease / Diameter normal reference × 100% or Areadisease/Area normal reference × 100%. However, in some instances,coronary vessels are diffusely diseased, which can render comparison ofdiseased, stenotic regions to “normal” regions of the vessel problematicand difficult when there is no normal region of the vessel withoutdisease to compare to.

In addition, in some of such embodiments, stenosis measurements can bereported in 2D, not 3D. Specifically, some embodiments rely upon imagingmethods which are two-dimensional in nature and thus, report outstenoses as relative % area narrowing (2D) or relative % diameternarrowing (2D). Some of such embodiments do not account for the markedirregularity in coronary artery lesions that are often present and donot provide information about the coronary artery lesion across thelength of a vessel. In particular, if the x-axis is considered the axialdistance along a coronary vessel, the y-axis the width of an arterywall, and the z-axis the irregular topology of plaque along the lengthof a vessel, then it can become evident that a single % area narrowingor a single % diameter narrowing is inadequate to communicate thecomplexity of the coronary lesion.

In some of such embodiments, because % area and % diameter stenosis arebased upon 2D measurements, certain methods that define stenosisseverity can rely upon maximum % stenosis rather than the stenosisconferred by three-dimensional coronary lesions that demonstrateheterogeneity in length and degree of narrowing across their length(i.e., volume). As such, in some of such embodiments, tracking over timecan be difficult (e.g., monitoring the effects of therapy) where changesin 2D would be much less accurate. A similar analogy can be whenevaluating changes in a pulmonary nodule while the patient is in followup, which can be much more accurate in 3D than 2D.

Furthermore, in some of such embodiments, the natural tapering ofarteries may not be accounted for any and/or all forms of imaging. Asillustrated in FIG. 24A, the coronary arteries can naturally get smalleralong their length. This can be problematic for % area and % diametermeasurements, as these approaches may not take into account that anormal coronary artery tapers gradually along its length. Hence, in someof such embodiments, the comparison to a normal reference diameter ornormal reference area has been to use the most normal appearing vesselsegment/cross-section proximal to a lesion. In this case, because theproximal cross-section is naturally larger (due to the tapering), theactual % narrowing (by area or diameter) can be lower than it actuallyis.

As such, in some of such embodiments, there are certain limitations tograding of coronary artery stenosis. Thus, it can be advantageous toaccount for the diffuseness of disease in a volumetric fashion, whilstaccounting for natural vessel tapering, as in certain other embodimentsdescribed below. Instead, in some of such embodiments described above,certain formulas can be used to evaluate these phenomena in 2 dimensionsrather than 3 dimensions, in which the relative degree of narrowing,also called stenosis or maximum diameter reduction, is determined bymeasuring the narrowest lumen diameter in the diseased segment andcomparing it to the lumen diameter in the closest adjacent proximaldisease-free section. In some of such embodiments, this is because withplaque present it can be no longer possible to measure directly what thelumen diameter at that point was originally.

Similarly, in some of such embodiments, the remodeling index can beproblematic. In particular, in some of such embodiments, the remodelingindex is determined by measuring the outer diameter of the vessel andthis is compared to the diameter in the closest adjacent proximaldisease-free section. In some of such embodiments, on CT imaging, thenormal coronary artery wall is not resolved as it’s thickness of -0.3 mmis beyond the ability of being depicted on CT due to resolutionlimitations.

Some examples of these problems in some of such embodiments areillustrated in FIGS. 24B-G and accompanying text. For example, FIG. 24Billustrates such an embodiment(s) of determining % stenosis andremodeling index. In the illustrated embodiment(s), it is assumed thatthe diameter of the closest adjacent proximal disease-free section (R)accurately reflects what the diameter at the point of stenosis oroutward remodeling would be. However, this simple formula maysignificantly overestimate the actual stenosis and underestimate theremodeling index. In particular, these simple formulas may not take intoaccount that a normal coronary artery tapers gradually along its lengthas depicted in FIG. 24C. As illustrated in FIG. 24C, the coronarydiameter may not be constant, but rather the vessel can taper graduallyalong its course. For example, the distal artery diameter (D2) may beless than 50% or more of the proximal diameter (D1).

Further, when there is a long atherosclerotic plaque present, thereference diameter R0 measured in a “normal” proximal part of the vesselmay have a significantly larger diameter than the diameter that wasinitially present, especially when the measured stenosis or remodelingindex is positioned far from the beginning of the plaque. This canintroduce error into the Stenosis % equation, resulting in a percentdiameter stenosis larger and remodeling index significantly lower thanit should be. As illustrated in FIG. 24D, when there is a long plaquepositioned proximal to the point of maximal stenosis (Lx) or positiveremodeling (Wx), in some of such embodiments, the reference diameter R0can be currently measured in the closest normal part of the vessel;however at this point the vessel can be significantly larger than itwould have initially been at position x, introducing error.

Generally speaking, clinical decision making in cardiology is oftenguideline driven and decisions often take the quantitative percentstenosis or remodeling index into account. For example, in the case ofpercent stenosis, a threshold of 50 or 70% can be used to determine ifadditional diagnostic testing or intervention is required. As anon-limiting example, FIG. 24E depicts how an inaccurately estimated R0could significantly affect the resulting percent stenosis and remodelingindex. As illustrated in FIG. 24E, if the estimated R0 is larger thanthe true lumen at the site of stenosis or positive remodeling,significant error can be introduced.

In some embodiments, with current technology by imaging (including butnot limited to CT, MRI and others), the internal lumen (L) and outer (W)is continuously measurable along the entire length of a coronary artery.In some embodiments, when the lumen diameter is equal to the walldiameter, there is no atherosclerotic plaque present, the vessel is“normal.” Conversely, in some embodiments, when the wall diameter isgreater than the lumen diameter, plaque is present. This is illustratedin FIG. 24F. As illustrated in FIG. 24F, in some embodiments, both thelumen diameter and outer wall diameter are continuously measured usingcurrent imaging techniques, such as CT. In some embodiments, when L=Wthere is no plaque present.

In some embodiments, an estimated reference diameter can be calculatedcontinuously at every point in the vessel where plaque is present. Forexample, by using the R0 just before plaque, and a Rn just after the endof the plaque, the degree of tapering along the length of the plaque canbe calculated. In some embodiments, this degree of tapering is, in mostcases, linear; but may also taper in other mathematically-predictablefashions (log, quadratic, etc.) and hence, the measurements may betransformed by certain mathematical equations, as illustrated in FIG.24G. In some embodiments, using the formula in FIG. 24G, an Rx can thenbe determined at any position along the plaques length. In someembodiments, this assumes that the “normal” vessel would have tapered ina linear (or other mathematically predictable fashions) manner acrossits length. As illustrated in FIG. 24G, in some embodiments, thereference diameter can be better estimated continuously along the lengthof the diseased portion of the vessel as long as the diameter justbefore the plaque R0 and just after the plaque Rn is known.

In some embodiments, once the continuous Rx reference diameter isdetermined, a continuous percent stenosis and/or remodeling index acrossthe plaque and be easily calculated, for example using the following.

$\text{\% Stenosis}_{\text{x}} = \frac{\text{R}_{\text{x}} - \text{L}_{\text{x}}}{\text{R}_{\text{x}}}\text{X 100}$

$\text{Remodeling Index RI}_{\text{x}} = \frac{\text{W}_{\text{x}}}{\text{R}_{\text{x}}}$

More specifically, in some embodiments, since the continuous lumendiameter Lx and wall diameter Wx are already known, continuous valuesfor percent stenosis and remodeling index and be easily calculated oncethe Rx values have been generated.

As described above, in some embodiments, there are certain limitationsto calculating stenosis severity and remodeling index in two dimensions.Further, even as improved upon with the accounting of the vessel taperand presence of plaque in some embodiments, these approaches may stillbe limited in that they are reliant upon 2D (areas, diameters) ratherthan 3D measurements (e.g., volume). Thus, as described in someembodiments herein, an improvement to this approach may be to calculatevolumetric stenosis, volumetric remodeling, and/or comparisons ofcompartments of the coronary artery to each other in a volumetricfashion.

As such, in some embodiments, the systems, devices, and methodsdescribed herein are configured to calculate volumetric stenosis,volumetric remodeling, and/or comparisons of compartments of thecoronary artery to each other in a volumetric fashion, for example byutilizing one or more image analysis techniques to one or more medicalimages obtained from a subject using one or more medical imagingscanning modalities. In some embodiments, the system can be configuredto utilize a normalization device, such as those described herein, toaccount for differences in scan results (such as for example densityvalues, etc.) between different scanners, scan parameters, and/or thelike.

In particular, in some embodiments, volumetric stenosis is calculated asillustrated in FIGS. 24H and 24I. As illustrated in FIGS. 24H and 24I,in some embodiments, the system can be configured to analyze a medicalimage of a subject to identify one or more boundaries along a vessel.For example, in some embodiments, the system can be configured toidentify the theoretically or hypothetically normal boundaries of theartery wall in the case a plaque was not present. In some embodiments,the system can be configured to identify the lumen wall and, in theabsence of plaque, the vessel wall. In some embodiments, the system canbe configured to identify an area of interrogation (e.g., site ofmaximum obstruction). In some embodiments, the system can be configuredto determine a segment with the plaque.

Thus, in some embodiments as illustrated in FIG. 24I, % volumetricstenosis can be calculated by the following equation, which accounts forthe 3D irregularity of contribution of the plaque to narrowing the lumenvolume, whilst considering the normal vessel taper and hypotheticallynormal vessel wall boundary: Lumen volume accounting for plaque (whichcan be measured) / Volume of hypothetically normal vessel (which can becalculated) × 100% = Volumetric % stenosis.

In some embodiments, an alternative method for % volume stenosis can beto include the entire vessel volume (i.e., that which is measured ratherthan that which is hypothetical). This can be governed by the followingequation: Lumen volume accounting for plaque (which can be measured) /Volume of vessel (which can be measured) × 100% = Volumetric % stenosis.

In some embodiments, another alternative method for determining %volumetric stenosis is to include the entire artery (i.e., that which isbefore, at the site of, and after a narrowing), as illustrated in FIG.24I.

In some embodiments, the systems, devices, and methods described hereinare configured to calculate volumetric remodeling. In particular, insome embodiments, volumetric remodeling can account for the naturaltapering of a vessel, the 3D nature of the lesion, and/or the comparisonto a proper reference standard. FIG. 24J is a schematic illustration ofan embodiment(s) of determining volumetric remodeling. In the example ofFIG. 24J, the remodeling index of Lesion #1, that is 5.2 mm in length,is illustrated.

As illustrated in FIG. 24J, in some embodiments, the system can beconfigured to identify from a medical image a length of Lesion #1 inwhich a region of plaque is present (note the natural 8% taper by area,diameter or volume). In some embodiments, the system can be configuredto identify a lesion length immediately before Lesion #1 in a normalpart of the vessel (note the natural 12% taper by area, diameter orvolume). In some embodiments, the system can be configured to identify alesion length immediately after Lesion #1 in a normal part of the vessel(note the natural 6% taper by area, diameter or volume). In someembodiments, the system can be configured to identify one or moreregions of plaque. In some embodiments, the system can be configured toidentify or determine a 3D volume of the vessel across the lesion lengthof 5.2 mm immediately before and/or after Lesion #1 and/or in Lesion #1.

In some embodiments, the system can be configured to calculate aVolumetric Remodeling Index by the following: (Volume within Lesion #1had plaque not been present + Volume of plaque in Lesion #1 exterior tothe vessel wall) / Volume within Lesion #1 had plaque not been present.By utilizing this formula, in some embodiments, the resulting volumetricremodeling index can take into account tapering, as the volume withinlesion #1 had plaque not been present takes into account any effect oftapering.

In some embodiments, the Volumetric Remodeling Index can be calculatedusing other methods, such as: Volume within Lesion #1 had plaque notbeen present / Proximal normal volume immediately proximal to Lesion #1× 100%, mathematically adjusted for the natural vessel tapering. Thisvolumetric remodeling index uses the proximal normal volume as thereference standard.

Alternatively, in some embodiments, a method of determining volumetricremodeling index that does not directly account for natural vesseltapering can be calculated by Volume within Lesion #1 had plaque notbeen present / ((Proximal normal volume immediately proximal to Lesion#1 + Distal normal volume immediately distal to Lesion #1)) / 2 in orderto account for the natural tapering.

Further, in some embodiments, with the ability to evaluate coronaryvessels in 3D, along with the ability to determine thehypothetically-normal boundaries of the vessel wall even in the presenceof plaque, the systems, methods, and devices described herein can beconfigured to either measure (in the absence of plaque) or calculate thenormal coronary vessel blood volume.

For example, in some embodiments, this coronary vessel blood volume canbe assessed by one or more of the following: (1) Total coronary volume(which represents the total volume in all coronary arteries andbranches); (2) Territory- or Artery-specific volume, or % fractionalblood volume (which represents the volume in a specific artery orbranch); (3) Segment-specific volume (which represents the volume in aspecific coronary segment, of which there are generally considered 18segments); and/or within-artery % fractional blood volume (whichrepresents the volume in a portion of a vessel or branch, i.e., in theregion of the artery before a lesion, in the region of the artery at thesite of a lesion, in the region of the artery after a lesion, etc.).

FIG. 24K illustrates an embodiment(s) of coronary vessel blood volumeassessment based on total coronary volume. FIG. 24L illustrates anembodiment(s) of coronary vessel blood volume assessment based onterritory or artery-specific volume. For example, in the illustratedembodiment, the right the right coronary artery territory volume wouldbe the volume within #1, #2, #3, #4, and #5, while the right coronaryartery volume would be the volume within #1, #2, and #3. As an exampleof segment-specific volume-based assessment of coronary vessel bloodvolume, a segment-specific volume (e.g., mid-right coronary artery) canbe the volume in #2. FIG. 24M illustrates an embodiment(s) of coronaryvessel blood volume assessment based on within-artery % fractional bloodvolume, where the proximal and distal regions comprise portions of theartery fractional blood volume.

Numerous advantages exist for assessing fractional blood volume. In someembodiments, because this method allows for determination of coronaryvolume hypothetically-normal boundaries of the vessel wall even in thepresence of plaque, these approaches allow for calculation of the %blood volume conferring potential risk to myocardium—comes the abilityto either measure (in the absence of plaque) or calculate the normalcoronary vessel blood volume. FIG. 24N illustrates an embodiment(s) ofassessment of coronary vessel blood volume.

In some embodiments, based on one or more metrics described above, aswell as the ability to determine the hypothetically normal boundaries ofthe vessel, the systems, devices, and methods described herein can beconfigured to determine the ischemia-causing nature of a vessel by anumber of different methods.

In particular, in some embodiments, the system can be configured todetermine % vessel volume stenosis, for example by: Measured lumenvolume / Hypothetically normal vessel volume × 100%. This is depicted inFIG. 24O.

In some embodiments, the system can be configured to determine pressuredifference across a lesion using hypothetically normal artery,continuity equation and naturally occurring coronary flow rate rangesand/or other physiologic parameters. This is illustrated in FIG. 24P. Inthe embodiment illustrated in FIG. 24P, there is a plaque that extendsinto the lumen and narrows the lumen (at the maximum narrowing, it isR0). In some embodiments, the system can compare R0 to R-1, R-2, R-3 orany cross-section before the lesion.

In some embodiments, using this comparison, the system can apply thecontinuity equation either using actual measurements (e.g., at lines inFIG. 24P) or the hypothetically normal diameter of the vessel. Thecontinuity equation applied to the coronary arteries is illustrated inFIG. 24Q.

As illustrated in FIG. 24Q, in some embodiments, the system, by usingimaging (CT, MRI, etc.), can be configured to determine thecross-sectional area of artery at a defined point before the site ofmaximum narrowing (A1) and the cross-sectional area of artery at thesite of maximum narrowing (A2) with high accuracy. However, in someembodiments, velocity and velocity time integral are unknown. Thus, insome embodiments, the velocity time integral (VTI) at a defined pointbefore the site of maximum narrowing (V1) and the VTI at a defined pointafter the site of maximum narrowing (V2) are provided, for example incategorical outputs based upon what has been empirically measured forpeople at rest and during exertion (mild, moderate and extreme).

As a non-limiting example, at rest, the total coronary blood flow can beabout -250 ml/min (-0.8 ml/min*g of heart muscle), which represents -5%of cardiac output. At increasing levels of exertion, the coronary bloodflow can increase up to 5 times its amount (-1250 ml/min). Thus, in someembodiments, the system can categorize the flow into about 250 ml/min,about 250-500 ml/min, about 500-750 ml/min, about 750-1000 ml/min,and/or about 1250 ml/min. Other categorizations can exist, and thesenumbers can be reported in continuous, categorical, and/or binaryexpressions. Further, based upon the observations of blood flow, theserelationships may not necessarily be linear, and can be transformed bymathematical operations (such as log transform, quadratic transform,etc.).

Further, in some embodiments, other factors can be calculated based uponranges, binary expressions, and/or continuous values, such as forexample heart rate, aortic blood pressure and downstream myocardialresistance, arterial wall / plaque resistance, blood viscosity, and/orthe like. Empirical measurements of fluid behavior in these differingconditions can allow for putting together a titratable input for thecontinuity equation.

Further, in some embodiments, because imaging allows for evaluation ofthe artery across the entire cardiac cycle, measured (or assumed)coronary vasodilation can allow for time-averaged A1 and A2measurements.

As such, in some embodiments, the system can be configured to utilizeone or more of the following equations: (1) Q = area × velocity @ siteof maximum obstruction (across a range of flows observed in empiricalmeasurements); and (2) Q = area × velocity @ site proximal to maximumobstruction (across a range of flows observed in empiricalmeasurements).

From the assumed flows and measured areas, in some embodiments, thesystem can then back-calculate the velocity. Then, the system can applythe simplified or full Bernoulli’s equations to equal: Pressure change =4(V2-V1)². From this, in some embodiments, the system can calculate thepressure drop across a lesion and, of equal import, can assess thispressure change across physiologically-realistic parameters that apatient will face in real life (e.g., rest, mild/moderate/extremeexertion).

Further, in some embodiments, the system can apply a volumetriccontinuity equation to account for a volume of blood before and after alesion narrowing, such as for example: (1) Q = volume × velocity @ siteof maximum obstruction (across a range of flows observed in empiricalmeasurements); and (2) Q = volume × velocity @ site proximal to maximumobstruction (across a range of flows observed in empiricalmeasurements). From the assumed flows and measured volumes, in someembodiments, the system can then back-calculate the velocity and, ifassuming or measuring heart rate, the system can then back-calculate thevelocity time integral.

FIG. 24R is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determining volumetric stenosis and/orvolumetric vascular remodeling. As illustrated in FIG. 24R, in someembodiments, at block 2402 the system is configured to access one ormore medical images, for example from a medical image database 100. Theone or more medical images can be obtained using any one or more of theimaging modalities discussed herein. In some embodiments, at block 2404,the system can be configured to identify one or more segments ofarteries and/or regions of plaque by analyzing the medical image.

In some embodiments, the system at block 2406 can be configured todetermine a lumen wall boundary in the one or more segments where plaqueis present. In some embodiments, the system at block 2406 can beconfigured to determine a hypothetical normal artery boundary if plaquewere not present. In some embodiments, the system at block 2408 can beconfigured to quantify the lumen volume with plaque and/or ahypothetical normal vessel volume had plaque not been present. In someembodiments, using the foregoing, the system at block 2410 can beconfigured to determine volumetric stenosis of the one or more segments,taking into account tapering and true assessment of the vesselmorphology based on image analysis.

In some embodiments, the system at block 2412 can be configured toquantify the volume of one or mor eregions of plaque. For example, insome embodiments, the system can be configured to quantify for a segmentor lesion the total volume of plaque, volume of plaque inside thehypothetical normal artery boundary, volume of plaque outside thehypothetical normal artery boundary, and/or the like. In someembodiments, the system at block 2414 can be configured to utilize theforegoing to determine a volumetric remodeling index. For example, insome embodiments, the system can be configured to determine a volumetricremodeling index by dividing the sum of the hypothetical normal vesselvolume and the plaque volume outside the hypothetical normal arteryboundary by the hypothetical normal vessel volume.

In some embodiments, the system at block 2416 can be configured todetermine a risk of CAD for the subject, for example based on one ormore of the determined volumetric stenosis and/or volumetric vascularremodeling index.

FIG. 24S is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determining ischemia. As illustrated inFIG. 24S, in some embodiments, the system can access a medical image atblock 2402, identify one or more segments of arteries and/or region ofplaque at block 2404, and/or determine the lumen wall boundary whiletaking into account the present plaque and/or a hypothetical normalartery boundary if plaque were not present at block 2406. In someembodiments, at block 2418, the system can be configured to quantify aproximal and/or distal cross-sectional area and/or volume along anartery. For example, in some embodiments, the system can be configuredto quantify a proximal cross-sectional area and/or volume at a lesionthat is proximal to a lesion of interest. In some embodiments, thelesion of interest can include plaque and/or a maximum narrowing of avessel. In some embodiments, the system can be configured to quantify adistal cross-sectional area and/or volume of the lesion of interest.

In some embodiments, the system can be configured to apply an assumedvelocity of blood flow at the proximal section at block 2420. In someembodiments, the assumed velocity of blood flow can be prestored orpredetermined, for example based on different states, such as at rest,during mild exertion, during moderate exertion, during extreme exertion,and/or the like.

In some embodiments, at block 2422, the system can be configured toquantify the velocity of blood flow at the distal section, for exampleat the lesion that includes plaque and/or maximum narrowing of thevessel. In some embodiments, the system is configured to quantify thevelocity of blood flow at the distal section by utilizing the continuityequation. In some embodiments, the system is configured to quantify thevelocity of blood flow at the distal section by utilizing one or more ofthe quantified proximal cross-sectional area or volume, quantifieddistal cross-sectional area or volume, and/or assumed velocity of bloodflow at the proximal section.

In some embodiments, the system at block 2424 is configured to determinea change in pressure between the proximal and distal sections, forexample based on the assumed velocity of blood flow at the proximalsection, the quantified velocity of blood flow at the distal section,the cross-sectional area at the proximal section, and/or thecross-sectional area at the distal section. In some embodiments, atblock 2426, the system is configured to determine a velocity timeintegral (VTI) at the distal section, for example based on thequantified velocity of blood flow at the distal section. In someembodiments, the system at block 2428 is configured to determineischemia for the subject, for example based on one or more of thedetermined change in pressure between the proximal and distal sectionsand/or VTI at the distal section.

Determining Myocardial Infarction Risk and Severity

The systems and methods described herein can also be used fordetermining myocardial infarction risk and severity from image-basedquantification and characterization of coronary atherosclerosis. Forexample, various embodiments described herein relate to systems,methods, and devices for determining patient-specific indications ofmyocardial infarction risk and severity risk from image-basedquantification and characterization of coronary atherosclerosis burden,type, and/or rate of progression.

One innovation includes a computer-implemented method of determining amyocardial risk factor via an algorithm-based medical imaging analysisis provided, the method comprising performing a comprehensiveatherosclerosis and vascular morphology characterization of a portion ofthe coronary vasculature of a patient using information extracted frommedical images of the portion of the coronary vasculature of thepatient, performing a characterization of the myocardium of the patientusing information extracted from medical images of the myocardium of thepatient, correlating the characterized vascular morphology of thepatient with the characterized myocardium of the patient, anddetermining a myocardial risk factor indicative of a degree ofmyocardial risk from at least one atherosclerotic lesion.

Performing the comprehensive atherosclerosis and vascular morphologycharacterization of the portion of the coronary vasculature of thepatient can include identifying the location of the at least oneatherosclerotic lesion. Determining the myocardial risk factorindicative of the degree of myocardial risk from the at least oneatherosclerotic lesion can include determining a percentage of themyocardium at risk from the at least one atherosclerotic lesion.Determining a percentage of the myocardium at risk from the at least oneatherosclerotic lesion can include determining the percentage of themyocardium subtended by the at least one atherosclerotic lesion.Determining the myocardial risk factor indicative of the degree ofmyocardial risk from the at least one atherosclerotic lesion can includedetermining an indicator reflective of a likelihood that the at leastone atherosclerotic lesion will contribute to a myocardial infarction.

Performing the characterization of the myocardium of the patient caninclude performing a characterization of the left ventricular myocardiumof the patient. The method can further include correlating thedetermined myocardial risk factor to at least one risk of a severeclinical event. The method can further include comparing the determinedmyocardial risk factor to a second myocardial risk factor indicative ofa degree of myocardial risk to the patient at a previous point in time.

Another innovation includes a computer-implemented method of determininga segmental myocardial risk factor via an algorithm-based medicalimaging analysis is provided, the method comprising characterizingvascular morphology of the coronary vasculature of a patient usinginformation extracted from medical images of the coronary vasculature ofthe patient, identifying at least one atherosclerotic lesion within thecoronary vasculature of the patient using information extracted frommedical images of the portion of the coronary vasculature of thepatient, characterizing a plurality of segments of the myocardium of thepatient to generate a segmented myocardial characterization usinginformation extracted from medical images of the myocardium of thepatient, correlating the characterized vascular morphology of thepatient with the segmented myocardial characterization of the patient,and generating an indicator of segmented myocardial risk from the atleast one atherosclerotic lesion.

Generating an indicator of segmented myocardial risk can includegenerating a discrete indicator of myocardial risk for at least a subsetof the plurality of segments of the myocardium. Generating an indicatorof segmented myocardial risk can include generating a discrete indicatorof myocardial risk for each of the plurality of segments of themyocardium.

Correlating the characterized vascular morphology of the patient withthe segmented myocardial characterization of the patient can includeidentifying for each of the myocardial segments a coronary arteryprimarily responsible for supplying oxygenated blood to that myocardialsegment. The segmented myocardial characterization can be segmented into17 segments according to a standard AHA 17-segment model.

In another innovation, a computer-implemented method of determining asegmental myocardial risk factor via an algorithm-based medical imaginganalysis is provided, the method comprising applying at least a firstalgorithm to a first plurality of images of the coronary vasculature ofa patient obtained using a first imaging technology to characterize thevascular morphology of the coronary vasculature of the patient and toidentify a plurality of atherosclerotic plaque lesions, applying atleast a second algorithm to a first plurality of images of themyocardium of the patient obtained using a second imaging technology tocharacterize the myocardium of the patient, applying at least a thirdalgorithm to relate the characterized vascular morphology of the patientwith the characterized myocardium of the patient, and calculating apercentage of subtended myocardium at risk from at least one of theplurality of identified atherosclerotic plaque lesions.

The method can additionally include applying an algorithm to a secondplurality of images of the coronary vasculature of the patient obtainedusing a third imaging technology to characterize the vascular morphologyof the coronary vasculature of the patient and to identify a pluralityof atherosclerotic plaque lesions. Applying an algorithm to a secondplurality of images of the coronary vasculature of the patient caninclude applying the first algorithm to the second plurality of imagesof the coronary vasculature of the patient. The method can additionallyinclude applying an algorithm to a second plurality of images of themyocardium of the patient obtained using a third imaging technology tocharacterize the myocardium of the patient.

Applying at least the first algorithm to the first plurality of imagesof the coronary vasculature of a patient obtained using the firstimaging technology can additionally include determining characteristicsof the identified plurality of atherosclerotic plaque lesions. The canadditionally include determining a risk of the identified plurality ofatherosclerotic plaque lesions contributing to a myocardial infarction,and determining an overall risk indicator based on the determined riskof the identified plurality of atherosclerotic plaque lesionscontributing to a myocardial infarction and the calculated percentage ofsubtended myocardium at risk from the identified plurality ofatherosclerotic plaque lesions.

The method can additionally include relating the calculated percentageof subtended myocardium at risk from at least one of the plurality ofidentified atherosclerotic plaque lesions to a risk of at least oneadverse clinical events.

Overview

Various embodiments described herein relate to systems, methods, anddevices for determining patient-specific myocardial infarction (MI) riskindicators from image-based analysis of arterial atheroscleroticlesion(s).

The heart includes epicardial coronary arteries, vessels which transmitoxygenated blood from the aorta to the myocardium of the heart. Withinthese epicardial coronary arteries, atherosclerotic lesions can build updue to plaque accumulation. These atherosclerotic lesions can erode orrupture, dislodging plaque and leading to thrombotic occlusion of ablood vessel at a location distal of the atherosclerotic lesionlocation, leading to a myocardial infarction (MI) or major adversecardiovascular events (MACE), also known as a heart attack. During aheart attack, flow of oxygenated blood to the myocardium is impeded bythe thrombotic occlusion of the blood vessel, leading to damage,including irreversible damage, of the myocardium.

Myocardial damage may directly impact the ability of the heart muscle tocontract and/or relax normally, a condition which may lead to clinicallymanifest heart failure. Heart failure is a complex syndrome which mayaffect a patient in a number of ways. The quality of life may beimpaired, due to shortness of breath or other symptoms, and mortalitymay be accelerated. The contractile function of the heart may beimpaired in one or more aspects, including reduced ejection fraction,elevated left ventricular volumes, left ventricular non-viability, andmyocardial stunning, as well as abnormal heart rhythms, such asventricular tachyarrhythmias. Surgical intervention, including coronaryartery bypass surgery and heart transplants, may be needed, along withother invasive procedures, such as stent procedures.

The likelihood that a given atherosclerotic lesion may lead to amyocardial infarction or other major adverse cardiovascular event may bedependent, at least in part, on the properties of the lesion, includingthe nature of the accumulated plaque. The presence of fatty plaquebuildup can inhibit blood flow therethrough to a greater extent thancalcified plaque build-up. When an artery contains “good” or stableplaque, or plaque comprising hardened calcified content, the lesion maybe less likely to result in a life-threatening condition such as amyocardial infarction. In contrast, atherosclerotic lesions containing“bad” or unstable plaque or plaque comprising fatty material can be morelikely to rupture within arteries, releasing the fatty material into thearteries. Such a fatty material release in the blood stream can causeinflammation that may result in a blood clot. A blood clot in the arterycan cause a stoppage of blood flow to the heart tissue, which can resultin a heart attack or other cardiac event.

Evaluation of the nature of a given atherosclerotic lesion may be usedto make a determination as to whether a lesion contains “high-riskplaque” or “vulnerable plaque” which is likely to contribute to a futuremyocardial infarction. Although such predictions are not exact,evaluation of various characteristics of a given atherosclerotic lesionmay be used to classify the atherosclerotic lesion as being a high-riskplaque. These characteristics include, but are not limited to,atherosclerosis burden, composition, vascular remodeling, diffuseness,location, direction, and napkin-ring sign, among other characteristics.The evaluation may be based on medical imagery indicative of thecardiovascular system of a patient.

Various medical imaging processes may be used in the analyses describedherein. In some embodiments, invasive medical imaging may be used togather information regarding a given atherosclerotic lesion. In otherembodiments, however, non-invasive medical imaging may be used, such ascoronary computed tomographic angiography (CCTA), which allows directvisualization of coronary arteries in a non-invasive fashion.

In some embodiments, the characterization of atherosclerosis andvascular morphology may include the analysis of a series of CCTA imagesor any other suitable images, and the generation of a three-dimensionalmodel of a portion of the patient’s cardiovascular system. This analysiscan include the generation of one or more quantified measurements ofvessels from the raw medical image, such as for example diameter,volume, morphology, and/or the like. This analysis may segment thevessels in a predetermined manner, or in a dynamic manner, in order toprovide more detailed overview of the vascular morphology of thepatient.

In particular, in some embodiments, the system can be configured toutilize one or more AI and/or ML algorithms to automatically and/ordynamically identify one or more arteries, including for examplecoronary arteries, although other portions of a patient’s cardiovascularsystem may also be generated. In some embodiments, one or more AI and/orML algorithms use a neural network (CNN) that is trained with a set ofmedical images (e.g., CT scans) on which arteries and features (e.g.,plaque, lumen, perivascular tissue, and/or vessel walls) have beenidentified, thereby allowing the AI and/or ML algorithm to automaticallyidentify arteries directly from a medical image. In some embodiments,the arteries are identified by size and/or location.

This analysis can also include the identification and classification ofplaque within the cardiovascular system of the patient. In someembodiments, the system can be configured to identify a vessel wall anda lumen wall for each of the identified coronary arteries in the medicalimage. In some embodiments, the system is then configured to determinethe volume in between the vessel wall and the lumen wall as plaque. Insome embodiments, the system can be configured to identify regions ofplaque based on the radiodensity values typically associated withplaque, for example by setting a predetermined threshold or range ofradiodensity values that are typically associated with plaque with orwithout normalizing using a normalization device.

In some embodiments, the characterization of atherosclerosis may includethe generation of one or more quantified measurements from a raw medicalimage, such as for example radiodensity of one or more regions ofplaque, identification of stable plaque and/or unstable plaque, volumesthereof, surface areas thereof, geometric shapes, heterogeneity thereof,and/or the like. Using this plaque identification and classification,the overall plaque volume may be determined, as well as the amount ofcalcified stable plaque and the amount of uncalcified plaque. In someembodiments, more detailed classification of atherosclerosis than abinary assessment of calcified vs. non-calcified plaque may be made. Forexample, the plaque may be classified ordinally, with plaque classifiedas dense calcified plaque, calcified plaque, fibrous plaque, fibrofattyplaque, necrotic core, or admixtures of plaque types. The plaque mayalso be classified continuously, by attenuation density on a scale suchas a Hounsfield unit scale or a similar classification system.

The information which can be obtained in the characterization ofatherosclerosis may be dependent upon the type of imaging beingperformed. For example, when the CCTA images are creating using asingle-energy CT process, the relative material density of the plaquerelative to the surrounding tissue can be determined, but the absolutematerial density may be unknown. In contrast, when the CCTA images arecreating using a multi-energy CT process, the absolute material densityof the plaque and other surrounding tissue can be measured.

The characterization of atherosclerosis and vascular morphology mayinclude in particular the identification and classification ofatherosclerotic lesion within the cardiovascular system of the patient,and in certain embodiments within the coronary arteries of the patient.This may include the calculation or determination of a binary ornumerical indicator regarding one or more parameters of anatherosclerotic lesion, based on the quantified and/or classifiedatherosclerosis derived from the medical image. The system may beconfigured to calculate such indicators regarding one or more parametersof an atherosclerotic lesion using the one or more vascular morphologyparameters and/or quantified plaque parameters derived from the medicalimage of a coronary region of the patient. In some embodiments, thesystem is configured to dynamically identify an atherosclerotic lesionwithin an artery, and calculate information regarding theatherosclerotic lesion and the adjacent section of the vessel, such asvessel parameters including diameter, curvature, local vascularmorphology, and the shape of the vessel wall and the lumen wall in thearea of the atherosclerotic lesion.

Calculation of Myocardial Risk

FIG. 25A is a flowchart illustrating a process 2500 for determining anindicator of risk that an atherosclerotic lesion will contribute to amyocardial infarction or other major adverse cardiovascular event. Atblock 2505, a system can access a plurality of images indicative of aportion of a cardiovascular system of a patient. These images can be,for example, CCTA images or any other suitable images, and can begenerated at a medical facility. These images can be reflective of, forexample a portion of the cardiovascular system of a patient includingthe coronary arteries, and can be representative of at least an entirecardiac cycle. In some embodiments, these CCTA images may be reflectiveof the portion of the cardiovascular system of a patient both prior toand after exposure of the patient to a vasodilatory substance, such asnitroglycerin or iodinated contrast. These CCTA images can be reflectiveof one or more known physiologic condition of the patient, such as an atrest state or a hyperemic state.

At block 2510, the system can analyze the images to identify at leastone atherosclerotic lesion (e.g., artery abnormalities) within theportion of the cardiovascular system of the patient. Atheroscleroticlesions may develop predominantly at branches, bends, and bifurcationsin the arterial tree. Identifying the at least one atheroscleroticlesion within the portion of the cardiovascular system can includedetermining information on characteristics and parameters of theatherosclerotic lesion using any of the functionality described herein,for example, information on plaque and it characteristics/parameters,lesion size, lesion location, vessel and/or lumen size and shapeinformation, etc. This identification may be, for example, part of abroader characterization of atherosclerosis and vascular morphologybased on the plurality of images. A characterization of atherosclerosiscan include the identification of the location, volume and/or type ofplaque throughout the portion of the cardiovascular system of thepatient.

At block 2515, the system can apply an algorithm that analyzescharacteristics/parameters of the identified atherosclerotic lesion todetermining an indicator of risk that an atherosclerotic lesion willcontribute to a myocardial infarction or other major adversecardiovascular event. This analysis can include, for example, any ofatherosclerosis burden, composition, vascular remodeling, diffuseness,location, direction, and napkin-ring sign, among other characteristics,as well as any combination thereof. The napkin-ring sign refers to arupture-prone plaque in a coronary artery, comprising a necrotic corecovered by a thin cap fibro-atheroma.

In some embodiments, the indicator of risk may be a binary indicator,and the system may designate one or more analyzed atheroscleroticlesions as either being high-risk for a heart attack (myocardialinfarction (MI)) or other major cardiac event, or not being a high-riskfor an MI or other major cardiac event. In other embodiments, theindicator may be a numerical indicator providing a more granularindication of the decree of risk presented by a given atheroscleroticlesion. For example, a number from 1.0 (low) to 10.0 (high), or inanother example, from 1 (low) to 100 (high).

In some embodiments, multiple analyses may be used, using differentcombinations of parameters and/or different weightings of parameters,and multiple analyses of the same atherosclerotic lesion may be used inmaking an aggregate assessment of risk. For example, if any of themultiple analyses classify an atherosclerotic lesion as being high risk,the atherosclerotic lesion may be designated as high risk out ofcaution. In other embodiments, the indicators of risk from the variousanalyses may be averaged or otherwise combined into an aggregateindicator of risk.

While such an analysis may be used to provide a binary or numericalindication of a risk that a given atherosclerotic lesion may contributeto a myocardial infarction or other major adverse cardiovascular event,such an indicator, in isolation, may not provide an indication of alevel of risk associated with a myocardial infarction or other majoradverse cardiovascular event which would be caused by thatatherosclerotic lesion. An important factor in the overall level of riskto the health of a patient presented by a given atherosclerotic lesionis the location of that atherosclerotic lesion relative to thesurrounding portions of the cardiovascular.

FIG. 25B is schematic illustration of a human heart, illustratingcertain coronary arteries. The heart muscle 2520 is supplied withoxygenated blood from the aorta 2521 by the coronary vasculature, whichincludes a complex network of vessels ranging from large arteries toarterioles, capillaries, venules, veins, etc. Like all other tissues inthe body, the heart muscle 2520 needs oxygen-rich blood to function.Also, oxygen-depleted blood must be carried away. The coronary arterieswrap around the outside of the heart muscle 2520. Small branches extendinto the heart muscle 2520 to bring it blood.

The coronary arteries include the right coronary artery (RCA) 2525 whichextends from the aorta 2521 downward along the right side of the heart2520, and the left main coronary artery (LMCA) 2522 which extends fromthe aorta 2521 downward on the left side of the heart 2520. The RCA 2525supplies blood to the right ventricle, the right atrium, and the SA(sinoatrial) and AV (atrioventricular) nodes, which regulate the heartrhythm. The RCA 2525 divides into smaller branches, including the rightposterior descending artery and the acute marginal artery. Together withthe left anterior descending artery 2524, the RCA 2525 helps supplyblood to the middle or septum of the heart.

The LMCA 2522 branches into two arteries, the anterior interventricularbranch of the left coronary artery, also known as the left anteriordescending (LAD) artery 2524 and the circumflex branch of the leftcoronary artery 2523. The LAD artery 2524 supplies blood to the front ofthe left side of the heart 2520. The circumflex branch of the leftcoronary artery 2523 encircles the heart muscle. The circumflex branchof the left coronary artery 2523 supplies blood to the outer side andback of the heart, following the left part of the coronary sulcus,running first to the left and then to the right, reaching nearly as faras the posterior longitudinal sulcus.

Because the various coronary arteries supply blood to particular regionsof the heart, the impact of an interruption in the amount of oxygenatedblood passing through a given vessel caused by stenosis or occlusion isdependent upon the location of the vessel at which the stenosis orocclusion occurs. A stenosis or occlusion proximal the aorta and/orlocated in a larger vessel can impact a larger percentage of the heartmuscle 2520, and in particular the myocardium, than a stenosis orocclusion distal the aorta and/or located in a smaller vessel.

In some embodiments, the ischemic impact of a stenosis in a coronaryartery can be evaluated by relating blood flow within the coronaryarteries of a patient to the corresponding myocardium that the coronaryarteries subtend. In such ischemia imaging processes, coronary stenosiscan be evaluated to identify regions that may impede blood flow withinthe epicardial coronary arteries, and relate that impediment to bloodflow to the percentage of myocardium that is at risk of becomingischemic, or otherwise impacted by reduced blood supply.

The evaluation of impacted myocardium can be combined with theevaluation of the risk that a given lesion may cause a myocardialinfarction or other major adverse cardiovascular event in order toprovide an indictor of the risk to the broader cardiac health of apatient posed by a given atherosclerotic lesion. In some embodiments,this can be expressed in terms of a percentage of subtended myocardiumat risk (referred to herein as %SMAR), linking a given coronaryatherosclerotic plaque lesion location within a coronary artery to themyocardium subtended by the coronary artery distal of the lesionlocation.

FIG. 25C is a flowchart illustrating a process 2530 for determining anindicator of a myocardial risk posed by an atherosclerotic lesion. Atblock 2531, a system can access a plurality of images indicative of aportion of a cardiovascular system of a patient. These images can be,for example, the result of a contrast-enhanced CT scan performed of thepatient’s heart and cardiac arteries. In other embodiments, however,these images may be generated using a wide variety of other imagingtechniques, including but not limited to ultrasound, magnetic resonanceimaging, or nuclear testing. In addition, multiple imaging modalitiescan be used to enhance the analysis, as discussed in greater detailherein.

At block 2532, the system can determine a characterization ofatherosclerosis and vascular morphology based on the plurality ofaccessed images. The characterization of the vascular morphology caninclude, for example, the automated extraction and labeling of thecoronary arteries, including the various branches and segments thereof.As in example, this labeling can include the identification and labelingof the centerlines of the various vessel segments to facilitate theextraction and labeling of the various segments. As in example, thislabeling can include the identification and labeling of the lumen andvessel walls of the various vessel segments to facilitate the extractionand labeling of the various segments. This characterization of thevascular morphology provides a patient-specific characterization of thevascular morphology of the patient. In particular, it can be used toprovide a patient-specific characterization of the coronary artery tree.

The system can also determine a characterization of atherosclerosiswithin the coronary vessels. In particular, the characterization ofatherosclerosis can include the automated identification ofatherosclerotic plaque lesions within the vasculature of the patient. Anumber of characteristic parameters of the identified atheroscleroticplaque lesions can be automatically calculated by the system, includingbut not limited to their volume, their composition, their remodeling,their location, and their relation to the myocardium of the patient. Theuse, for example, of a contrast enhanced CT scan allows theidentification by the system of the composition of the variousidentified atherosclerotic plaque lesions, such as by identifying themas primarily fatty plaque build-up or primarily calcified plaquebuild-up, as well as an indication of the density of the plaquebuild-up. Positive remodeling of the surrounding vessel in the locationof the identified atherosclerotic plaque lesions can also provide anindication of the risk posed by the identified atherosclerotic plaquelesions.

Although illustrated as a single block 2532, the characterization of thevascular morphology can be performed in separate steps and in anysuitable order. For example, in some embodiments, furthercharacterization of the vascular morphology may be performed based atleast in part on the characterization of atherosclerosis, withadditional analysis applied to portions of the vasculature of thepatient affected by the identified atherosclerotic plaque lesions.

At block 2533, the system can determine a characterization of themyocardium of the patient based on a plurality of accessed images. Insome embodiments, the myocardium of the patient may be characterizedusing one or more of the same accessed images used for thecharacterization of atherosclerosis and vascular morphology, while inother embodiments, medical imagery obtained via a different imagingtechnique may be used in the characterization of the myocardium. In someembodiments, a cardiac MRI or other imaging technique may be used togenerate images used for characterization of the myocardium.

In some embodiments, the characterization of the myocardium may be acharacterization of only a portion of the myocardium of the patient, ormay be a characterization which focuses primarily on certain regions ofthe myocardium, such as the left ventricular myocardium, due to theincreased thickness of the myocardium in the left ventricle. Thischaracterization may include, for example, the relative and absolutesize of the ventricular mass, as well as the overall shape of theventricular mass.

At block 2534, the system relates the characterization of the vascularmorphology to the characterization of the myocardium to provide apatient-specific characterization of the relationship between thepatient-specific vascular morphology characterization and the patientspecific myocardium characterization. Because there can be significantdifferences between patients in terms of the blood supply from specificcoronary arteries to various portions of the myocardium, the relation ofthe patient-specific vascular morphology characterization to thecharacterization of the myocardium can be used to more accuratelypredict the impact on the myocardium of an occlusion or other stenosisat a given location within the patient-specific vasculature.

This relation between the patient-specific vascular morphologycharacterization and the patient specific myocardium characterizationcan include relating the identified atherosclerotic plaque lesionswithin the vasculature of the patient to the characterization of themyocardium. The relation can include, for example, one or moreatherosclerosis metrics in this relation, including the volume andcomposition, of the atherosclerotic plaque lesions, as well as thepercent atheroma volume, the percentage of total vessel wall occupied bythe atherosclerotic plaque. The remodeling of the surrounding vesselwall may also be taken into account in this analysis.

At block 2535, the system determines an indicator of the amount of themyocardium at risk for a given atherosclerotic plaque lesion. Thisindicator may be, for example, a measure of the subtended myocardium atrisk from that atherosclerotic plaque lesion. The myocardium at risk maybe calculated or estimated based on the percentage of the myocardiumthat is subtended by the coronary artery at and distal the point of theatherosclerotic plaque lesion. In other embodiments, this indicator maybe a binary or numerical indicator which may be based on the percentageof the subtended myocardium at risk, but may also take into accountother factors, such as a likelihood that a given atherosclerotic plaquelesion will lead to an MI or similar event. By determining an indicatorbased at least in part on the subtended myocardium percentage, a broaderindication of the risk to a patient’s cardiovascular health can beprovided.

The use of such an indicator allows further tailoring of patientdiagnosis and treatment based upon a patient-specific indication of thedegree of risk posed by an MI or other major cardiac event caused by agiven atherosclerotic lesion. If a given atherosclerotic lesion mayrepresent a high risk to result in an MI or other major cardiac event,but only a small percentage of the myocardium, such as 2% of themyocardium (or e.g., less than 5%), is subtended by the lesion and atrisk, less drastic medical treatment, such as medical therapy, may beprescribed to the patient, rather than invasive percutaneous proceduressuch as stent placement or bypass surgery. In contrast, if a givenatherosclerotic lesion subtends a comparatively high percentage of themyocardium, such as 20% of the myocardium (or e.g., more than 20%),percutaneous intervention to seal or bypass the legion may beprescribed, as the intervention would be expected to result in asignificant reduction of risk of adverse consequences associated with anMI or other severe event. This may be the case even when the risk ofsuch an MI or other severe event is comparatively low, due to the dangerto a substantial percentage of the myocardium posed by thatatherosclerotic lesion.

In some embodiments, the characterization of the myocardium may includea segmented analysis of specific segments of the myocardium. FIG. 25D isa flowchart illustrating a process 2540 for determining an indicator ofa segmental myocardial risk posed by an atherosclerotic lesion. At block2541, a system can access a plurality of images indicative of a portionof a cardiovascular system of a patient. These images can be, forexample, the result of a contrast-enhanced CT scan performed of thepatient’s heart and cardiac arteries, but may also include imagesgenerated by another imaging technique.

At block 2542, the system can determine a characterization ofatherosclerosis and vascular morphology based on the plurality ofaccessed images. The characterization of the vascular morphology caninclude, for example, the automated extraction and labeling of thecoronary arteries, including the various branches and segments thereof,as well as the automatic identification of atherosclerotic plaquelesions within the vascular morphology.

At block 2543, the system can determine a characterization of one ormore segments of the myocardium of the patient based on a plurality ofaccessed images. In some embodiments, the myocardium of the patient maybe characterized using the same accessed images used for thecharacterization of atherosclerosis and vascular morphology, while inother embodiments, medical imagery obtained via a different imagingtechnique may be used in the characterization of the myocardiumsegments, such as a cardiac MRI or intracardiac echocardiography.

In some embodiments, the myocardium may be segmented according to astandard AHA 17-segment model. The AMA 17-segment model divides the leftventricle vertically into a basal section, a mid-cavity section, and anapical section, each of which is radially subdivided into additionalsegments. The basal segment is divided into six radial segments, thebasal anterior, basal anteroseptal, basal inferoseptal, basal inferior,basal inferolateral, and basal anterolateral. The mid-cavity issimilarly divided into six radial segments, the mid-anterior,mid-anteroseptal, mid-inferoseptal, mid inferior, mid-inferolateral, andmid-anterolateral. The tapered apical segment is divided into fourradial segments, the apical anterior, apical septal, apical inferior,and apical lateral. The apical cap, or apex, is analyzed as a singlecontiguous segment. The AHA 17-segment model is one example of asegmentation model which can be used to characterize the myocardium,although any other suitable segmentation model may also be used.

Due to the symmetrical radial segmentation, segmental characterizationof the myocardium according to the AHA 17-segment model can provide areproducible segmentation which can be used to monitor changes in themyocardium of a patient over time, compare the myocardialcharacteristics in various states for a given patent, and comparepatients to one another. The regular segmentation can also facilitatethe analysis of prior myocardial characterizations, even if notgenerated using the same system.

Under the standard AHA model, certain segments of the myocardium can beconsidered to generally be provided with blood by a specific coronaryartery of the left anterior descending artery, right coronary artery,and left circumflex artery, with a larger percentage of the segmentsbeing considered to be provided with blood by the left anteriordescending artery. For example, occlusion of the left anteriordescending is often called the widow-maker infarction, due to the severeimpact it can have on the operation of the heart. However, there can besignificant variation on a patient-by-patient basis due to the specificcardiovascular anatomy of each patient. For example, the apex segmentcan be provided with blood by any of the left anterior descending, rightcoronary artery, and left circumflex artery. Other segments can beprimarily provided with oxygenated blood by different coronary arteriesin different patients.

In other embodiments, alternative segmentation patterns may be used, andin some embodiments, the myocardium may be dynamically segmented for thepurposes of characterization. Such dynamic segmentation may, forexample, take into account the patient-specific vasculaturecharacterization to identify segments of the myocardium within which agiven vessel is likely to supply the majority of the oxygenated blood.Such dynamic segmentation can also be used as part of an iterativeprocess once the vasculature characterization is related to an initialmyocardial characterization.

At block 2544, the system relates the characterization of the vascularmorphology to the segmented characterization of the myocardium toprovide a patient-specific characterization of the relationship betweenthe patient-specific vascular morphology characterization and thepatient-specific characterization of at least one segment of themyocardium. In some embodiments, the characterization of all segments ofthe myocardium are related to the characterization of the vascularmorphology. By providing a patient-specific relation of thecharacterization of the vascular morphology to the segmentedcharacterization of the myocardium, the system may be able to moreaccurately model the impacted regions of the myocardium of a givenpatient than would be possible using a standardized association betweenthe myocardial segments and the coronary vessels.

At block 2545, the system determines an indicator of the segmentalmyocardial risk for a given atherosclerotic plaque lesion. In someembodiments, the indicator of the segmental myocardial risk may includean identification of the myocardial segments which are at leastpartially subtended by the atherosclerotic plaque lesion, and at riskfrom an MI or other severe cardiac event caused by the atheroscleroticplaque lesion. In some embodiments, a percentage of subtended myocardiumat risk (e.g., “%SMAR”) for each of the analyzed myocardial segments maybe generated, which may provide a more precise indication of the risksposed by a given atherosclerotic plaque lesion.

In addition to or in place of risk indicators relating to the risksposed by given atherosclerotic plaque lesions, overall risk factors mayalso be determined which are indicative of the risks posed by aplurality of atherosclerotic plaque lesions, or by all identifiedatherosclerotic plaque lesions. In some embodiments, such an overallrisk factor may include a cumulative %SMAR value for all identifiedatherosclerotic plaque lesions. In some embodiments, risk indicatorsassociated with the various identified atherosclerotic plaque lesionsmay be weighted or otherwise used in the calculation of a cumulativerisk indicator.

In some embodiments, the %SMAR or other risk indicator based thereon maybe related to a risk of adverse clinical events. FIG. 25E is a flowchartillustrating a process 2550 for determining a risk of adverse clinicalevents caused by an atherosclerotic lesion. At block 2551, a systemdetermines characterizations of atherosclerosis, vascular morphology,and myocardium of a patient based on one or more pluralities of accessedimages. The characterization of atherosclerosis can include theidentification of at least one atherosclerotic lesion within thevasculature of a patient. The characterization of the myocardium mayinclude the characterization of discrete sections of the myocardium.

At block 2552, the system correlates the characterization of thevascular morphology to the characterization of the myocardium to providea patient-specific characterization of the relationship between thevascular morphology characterization and the myocardialcharacterization.

At block 2553, the system calculates a percentage of myocardium at riskfrom at least one atherosclerotic plaque lesion. In some embodiments,the calculated percentage is reflective of the percentage of the entiremyocardium at risk. In some embodiments, the calculated percentage isreflective of the percentage of one or more segments of the myocardium.

At block 2554, the system can relate the calculated percentage ofmyocardium at risk to a risk of one or more adverse clinical events. Insome embodiments, the risk may be calculated for each of a plurality ofadverse clinical events. In some embodiments, the adverse clinicalevents may include reductions in quality of life, such as shortness ofbreath. In some embodiments, these adverse clinical events may includesevere clinical events such as accelerated mortality, a need forpercutaneous coronary revascularization such as a stent procedure, or aneed for heart transplant or coronary artery bypass surgery. In someembodiments, these adverse clinical effects may relate to reducedcontractile function, such as low ejection fraction, elevated leftventricular volumes, left ventricular non-viability, and myocardialstunning. In some embodiments, these adverse clinical events may includeabnormal heart rhythms such as ventricular tachyarrhythmias.

In some embodiments, a risk indicator based on percentage of myocardiumat risk may be reevaluated after some time has elapsed, or aftertreatment has been carried out. FIG. 25F is a flowchart illustrating aprocess 2560 for updating a risk of adverse clinical events caused by anatherosclerotic lesion. At block 2561, a system accesses informationindicative of the state of a portion of a cardiovascular system of apatient at a first point in time, as well as a plurality of imagesindicative of the state of the portion of the cardiovascular system ofthe patient at a second point in time after the first point in time. Insome embodiments, the second point in time may be a recent point intime, such that the reevaluated risk indicator will be indicative of therisk of the patient at the current point in time.

In some embodiments, the information indicative of the state of theportion of the cardiovascular system of the patient at the first pointin time can include a previously calculated risk indicator. In suchembodiments, the in other embodiments, the information indicative of thestate of the portion of the cardiovascular system of the patient at thefirst point in time can include a plurality of images indicative of thestate of the portion of the cardiovascular system of the patient at thefirst point in time, and a risk factor indicative of the state of theportion of the cardiovascular system of the patient at the first pointin time can be determined at the same time as the updated risk factorreflective of the state of the patient at the second point in time.

At block 2562, the system determines characterizations ofatherosclerosis, vascular morphology, and myocardium of the patientbased on the plurality of accessed images indicative of the state of thepatient at the second point in time. If the information indicative ofthe state of the portion of the cardiovascular system of the patient atthe first point in time includes a plurality of images indicative of thestate of the portion of the cardiovascular system of the patient at thefirst point in time, the system may also determine characterizations ofatherosclerosis, vascular morphology, and myocardium of the patientbased on the plurality of accessed images indicative of the state of thepatient at the first point in time. In an embodiment in which the systemuses an AI or ML algorithm to determine these characterizations,redetermination of the characteristics of the patient at the first pointin time can ensure consistency between these determinations, in theevent that the AI or ML algorithm has been updated or otherwise altered,such as due to the analysis of additional data, in the intervening time.

At block 2563, the system correlates the characterization of thevascular morphology to the characterization of the myocardium to providea patient-specific characterization of the relationship between thevascular morphology characterization and the myocardial characterizationat the second point in time. If the information indicative of the stateof the portion of the cardiovascular system of the patient at the firstpoint in time includes a plurality of images indicative of the state ofthe portion of the cardiovascular system of the patient at the firstpoint in time, the system may also correlate the characterization of thevascular morphology to the characterization of the myocardium to providea patient-specific characterization of the relationship between thevascular morphology characterization and the myocardial characterizationat the first point in time.

In an embodiment in which two such correlations are made atsubstantially the same point in time, or in which the informationindicative of the state of the portion of the cardiovascular system ofthe patient at the first point in time includes an indication of apreviously determined correlation, the system may compare thecorrelation at the first point in time to the correlation at the secondpoint in time, to determine if the vasculature or myocardium of thepatent has significantly changed if. If so, additional analysisregarding the cause for such a change may be performed, either by thesystem itself, or by a clinical practitioner evaluating the patient whocan be alerted to this discrepancy by the system.

At block 2564, the system calculates a percentage of myocardium at riskfrom at least one atherosclerotic plaque lesion at the second point intime. If the information indicative of the state of the portion of thecardiovascular system of the patient at the first point in time includesa plurality of images indicative of the state of the portion of thecardiovascular system of the patient at the first point in time, thesystem may also calculate a percentage of myocardium at risk from atleast one atherosclerotic plaque lesion at the first point in time.

At block 2565, the system compares the percentage of the myocardium atrisk at the first point in time to the calculated percentage of themyocardium at risk at the second point of time. In some embodiments,this comparison may provide a practitioner with information regardingthe efficacy of an intervening treatment of the patient, such as a stentprocedure or the use of statins which can solidify previously fattyplaque deposits. In some embodiments, this comparison may provide apractitioner with information regarding an updated prognosis for thepatient based upon more recent characterizations of the atherosclerosis,vascular morphology, and/or myocardium of the patient.

In some embodiments, the process may proceed to an additional step wherethe risk of one or more adverse clinical events can be updated basedupon the updated calculated percentage of the myocardium subtended by agiven lesion or a plurality of lesions. In addition, where prior imaginginformation is available, images from different points in time may befused together or otherwise used to generate a composite image or otherrepresentation indicative of changes over time. These changes can insome embodiments be due to interventions such as medication, exercise,or other medical procedures.

In some embodiments, as discussed herein, different imaging techniquesmay be used to characterize the atherosclerosis and vascular morphologythan those used to characterize the myocardium of the patient. However,in other embodiments, multiple imaging techniques may be used in any ofthese individual characterizations, as well. For example, the system mayanalyze CT imagery to extract information indicative of atherosclerosis,while the system may analyzed information extracted from positronemission tomography (PET) imagery to extract information indicative ofinflammation. By synthesizing information from multiple imagingmodalities, the disclosed technology can be used to enhance thephenotypic richness of the particular potion of the body beingcharacterized.

Although described herein primarily in the context of imaging andanalysis of the coronary arteries, the systems, methods and devices ofthe disclosed technology can also be used in the context of otherportions of the body, including other arterial beds. For example, thedisclosed technology can be used with ultrasound imagery of the carotidarterial bed, the aorta, and the arterial beds of the lower extremities,among other portions of the cardiovascular system of the patient. Thedisclosed technology may be used with any suitable imaging technology orcombination of imaging technologies, including but not limited to CT,ultrasound, MRI, PET, and nuclear testing.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 25G. The example computer system 2572 is incommunication with one or more computing systems 2590 and/or one or moredata sources 2592 via one or more networks 2586. While FIG. 25Gillustrates an embodiment of a computing system 2572, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 2572 can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 2572 can comprise a Patient-Specific Myocardial RiskDetermination Module 2584 that carries out the functions, methods, acts,and/or processes described herein. The patient-Specific Myocardial RiskDetermination Module 2584 is executed on the computer system 2572 by acentral processing unit (e.g., one or more hardware processors) 2576discussed further below.

In general the word “module,” as used herein, refers to logic embodiedin hardware or firmware or to a collection of software instructions,having entry and exit points. Modules are written in a program language,such as JAVA, C, or C++, or the like. Software modules can be compiledor linked into an executable program, installed in a dynamic linklibrary, or can be written in an interpreted language such as BASIC,PERL, LAU, PHP or Python and any such languages. Software modules can becalled from other modules or from themselves, and/or can be invoked inresponse to detected events or interruptions. Modules implemented inhardware include connected logic units such as gates and flip-flops,and/or can include programmable units, such as programmable gate arraysor processors.

Generally, the modules described herein refer to logical modules thatcan be combined with other modules or divided into sub-modules despitetheir physical organization or storage. The modules are executed by oneor more computing systems, and can be stored on or within any suitablecomputer readable medium, or implemented in-whole or in-part withinspecial designed hardware or firmware. Not all calculations, analysis,and/or optimization require the use of computer systems, though any ofthe above-described methods, calculations, processes, or analyses can befacilitated through the use of computers. Further, in some embodiments,process blocks described herein can be altered, rearranged, combined,and/or omitted.

The computer system 2572 includes one or more processing units (CPU)706, which can comprise a microprocessor. The computer system 2572further includes a physical memory 2580, such as random access memory(RAM) for temporary storage of information, a read only memory (ROM) forpermanent storage of information, and a mass storage device 2574, suchas a backing store, hard drive, rotating magnetic disks, solid statedisks (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory,diskette, or optical media storage device. Alternatively, the massstorage device can be implemented in an array of servers. Typically, thecomponents of the computer system 2572 are connected to the computerusing a standards based bus system. The bus system can be implementedusing various protocols, such as Peripheral Component Interconnect(PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) andExtended ISA (EISA) architectures.

The computer system 2572 includes one or more input/output (I/O) devicesand interfaces 2582, such as a keyboard, mouse, touch pad, and printer.The I/O devices and interfaces 2582 can include one or more displaydevices, such as a monitor, that allows the visual presentation of datato a user. More particularly, a display device provides for thepresentation of GUIs as application software data, and multi-mediapresentations, for example. The I/O devices and interfaces 2582 can alsoprovide a communications interface to various external devices. Thecomputer system 2572 can comprise one or more multi-media devices 2578,such as speakers, video cards, graphics accelerators, and microphones,for example.

Computing System Device / Operating System

The computer system 2572 can run on a variety of computing devices, suchas a server, a Windows server, a Structure Query Language server, a UnixServer, a personal computer, a laptop computer, and so forth. In otherembodiments, the computer system 2572 can run on a cluster computersystem, a mainframe computer system and/or other computing systemsuitable for controlling and/or communicating with large databases,performing high volume transaction processing, and generating reportsfrom large databases. The computing system 2572 is generally controlledand coordinated by an operating system software, such as z/OS, Windows,Linux, UNIX, BSD, PHP, SunOS, Solaris, MacOS, ICloud services or othercompatible operating systems, including proprietary operating systems.Operating systems control and schedule computer processes for execution,perform memory management, provide file system, networking, and I/Oservices, and provide a user interface, such as a graphical userinterface (GUI), among other things.

Network

The computer system 2572 illustrated in FIG. 25G is coupled to a network2588, such as a LAN, WAN, or the Internet via a communication link 2586(wired, wireless, or a combination thereof). Network 2588 communicateswith various computing devices and/or other electronic devices. Network2588 is in communication with one or more computing systems 2590 and oneor more data sources 2592. The Patient-Specific Myocardial RiskDetermination Module 2584 can access or can be accessed by computingsystems 2590 and/or data sources 2592 through a web-enabled user accesspoint. Connections can be a direct physical connection, a virtualconnection, and other connection type. The web-enabled user access pointcan comprise a browser module that uses text, graphics, audio, video,and other media to present data and to allow interaction with data viathe network 2588.

The output module can be implemented as a combination of an all-pointsaddressable display such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. The output module can be implemented to communicate with inputdevices 2582 and they also include software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements, such as menus, windows, dialogue boxes, tool bars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the output module can communicate with a set ofinput and output devices to receive signals from the user.

Other Systems

The computing system 2572 can include one or more internal and/orexternal data sources (for example, data sources 2592). In someembodiments, one or more of the data repositories and the data sourcesdescribed above can be implemented using a relational database, such asDB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well asother types of databases such as a flat-file database, an entityrelationship database, and object-oriented database, and/or arecord-based database.

The computer system 2572 can also access one or more data sources (ordatabases) 2592. The databases 2592 can be stored in a database or datarepository. The computer system 2572 can access the one or moredatabases 2592 through a network 2588 or can directly access thedatabase or data repository through I/O devices and interfaces 2582. Thedata repository storing the one or more databases 2592 can reside withinthe computer system 2572.

Examples of Embodiments Relating to Myocardial Infarction Risk andSeverity From Image-Based Quantification and Characterization ofCoronary Atherosclerosis

The following are non-limiting examples of certain embodiments ofsystems and methods for determining myocardial infarction risk andseverity and/or other related features. Other embodiments may includeone or more other features, or different features, that are discussedherein.

Embodiment 1: A computer-implemented method of determining a myocardialrisk factor via an algorithm-based medical imaging analysis, comprising:performing a atherosclerosis and vascular morphology characterization ofa portion of the coronary vasculature of a patient using informationextracted from medical images of the portion of the coronary vasculatureof the patient; performing a characterization of the myocardium of thepatient using information extracted from medical images of themyocardium of the patient; correlating the characterized vascularmorphology of the patient with the characterized myocardium of thepatient; and determining a myocardial risk factor indicative of a degreeof myocardial risk from at least one atherosclerotic lesion.

Embodiment 2: The method of embodiment 1, wherein performing theatherosclerosis and vascular morphology characterization of the portionof the coronary vasculature of the patient comprises identifying thelocation of the at least one atherosclerotic lesion.

Embodiment 3: The method of embodiment 1 or 2, wherein determining themyocardial risk factor indicative of the degree of myocardial risk fromthe at least one atherosclerotic lesion comprises determining apercentage of the myocardium at risk from the at least oneatherosclerotic lesion.

Embodiment 4: The method of embodiment 3, wherein determining apercentage of the myocardium at risk from the at least oneatherosclerotic lesion comprises determining the percentage of themyocardium subtended by the at least one atherosclerotic lesion.

Embodiment 5: The method of embodiment 3 or 4, determining themyocardial risk factor indicative of the degree of myocardial risk fromthe at least one atherosclerotic lesion comprises determining anindicator reflective of a likelihood that the at least oneatherosclerotic lesion will contribute to a myocardial infarction.

Embodiment 6: The method of any one of embodiments 1-5, whereinperforming the characterization of the myocardium of the patientcomprises performing a characterization of the left ventricularmyocardium of the patient.

Embodiment 7: The method of any one of embodiments 1-6, furthercomprising correlating the determined myocardial risk factor to at leastone risk of a severe clinical event, and/or correlating the determinedmyocardial risk factor to the severity of an event (for example,st-elevation myocardial infarction, non-ST elevation myocardialinfarction, unstable angina, stable angina, and the like).

Embodiment 8: The method of any one of embodiments 1-7, furthercomprising comparing the determined myocardial risk factor to a secondmyocardial risk factor indicative of a degree of myocardial risk to thepatient at a previous point in time.

Embodiment 9: A computer-implemented method of determining a segmentalmyocardial risk factor via an algorithm-based medical imaging analysis,comprising: characterizing vascular morphology of the coronaryvasculature of a patient using information extracted from medical imagesof the coronary vasculature of the patient; identifying at least oneatherosclerotic lesion within the coronary vasculature of the patientusing information extracted from medical images of the portion of thecoronary vasculature of the patient; characterizing a plurality ofsegments of the myocardium of the patient to generate a segmentedmyocardial characterization using information extracted from medicalimages of the myocardium of the patient; correlating the characterizedvascular morphology of the patient with the segmented myocardialcharacterization of the patient; and generating an indicator ofsegmented myocardial risk from the at least one atherosclerotic lesion.

Embodiment 10: The method of embodiment 9, wherein generating anindicator of segmented myocardial risk comprises generating a discreteindicator of myocardial risk for at least a subset of the plurality ofsegments of the myocardium.

Embodiment 11: The method of embodiment 9 or 10, wherein generating anindicator of segmented myocardial risk comprises generating a discreteindicator of myocardial risk for each of the plurality of segments ofthe myocardium.

Embodiment 12: The method of embodiment 9 or 11, wherein correlating thecharacterized vascular morphology of the patient with the segmentedmyocardial characterization of the patient comprises identifying foreach of the myocardial segments a coronary artery primarily responsiblefor supplying oxygenated blood to that myocardial segment.

Embodiment 13: The method of any one of embodiments 9-12, wherein thesegmented myocardial characterization is segmented into 17 segmentsaccording to a standard AHA 17-segment model.

Embodiment 14: A computer-implemented method of determining a segmentalmyocardial risk factor via an algorithm-based medical imaging analysis,comprising: applying at least a first algorithm to a first plurality ofimages of the coronary vasculature of a patient obtained using a firstimaging technology to characterize the vascular morphology of thecoronary vasculature of the patient and to identify a plurality ofatherosclerotic plaque lesions; applying at least a second algorithm toa first plurality of images of the myocardium of the patient obtainedusing a second imaging technology to characterize the myocardium of thepatient; applying at least a third algorithm to relate the characterizedvascular morphology of the patient with the characterized myocardium ofthe patient; and calculating a percentage of subtended myocardium atrisk from at least one of the plurality of identified atheroscleroticplaque lesions.

Embodiment 15: The method of embodiment 14, additionally comprisingapplying an algorithm to a second plurality of images of the coronaryvasculature of the patient obtained using a third imaging technology tocharacterize the vascular morphology of the coronary vasculature of thepatient and to identify a plurality of atherosclerotic plaque lesions.The third imaging technology can be, for example, intracardiacechocardiography, MRI, and any other suitable technology that cangenerate images that depict the vascular morphology of the coronaryvasculature of the patient and to identify a plurality ofatherosclerotic plaque lesions.

Embodiment 16: The method of embodiment 15, wherein applying analgorithm to a second plurality of images of the coronary vasculature ofthe patient comprises applying the first algorithm to the secondplurality of images of the coronary vasculature of the patient.

Embodiment 17: The method of embodiment 14, additionally comprisingapplying an algorithm to a second plurality of images of the myocardiumof the patient obtained using a third imaging technology to characterizethe myocardium of the patient.

Embodiment 18: The method of any one of embodiments 14-17, whereinapplying at least the first algorithm to the first plurality of imagesof the coronary vasculature of a patient obtained using the firstimaging technology additionally comprises determining characteristics ofthe identified plurality of atherosclerotic plaque lesions.

Embodiment 19: The method of embodiment 18, additionally comprisingdetermining a risk of the identified plurality of atherosclerotic plaquelesions contributing to a myocardial infarction, and determining anoverall risk indicator based on the determined risk of the identifiedplurality of atherosclerotic plaque lesions contributing to a myocardialinfarction and the calculated percentage of subtended myocardium at riskfrom the identified plurality of atherosclerotic plaque lesions.

Embodiment 20: The method of any one of embodiments 14-19, additionallycomprising relating the calculated percentage of subtended myocardium atrisk from at least one of the plurality of identified atheroscleroticplaque lesions to a risk of at least one adverse clinical events.

Combining CFD-Based Evaluation With Atherosclerosis and VascularMorphology

Various embodiments described herein relate to systems, methods, anddevices for medical image analysis, diagnosis, risk stratification,decision making and/or disease tracking. One innovation includes acomputer-implemented method of identifying a presence and/or degree ofischemia via an algorithm-based medical imaging analysis is provided,the method including performing a computational fluid dynamics (CFD)analysis of a portion of the coronary vasculature of a patient usingimaging data of the portion of the coronary vasculature of the patient,performing a comprehensive atherosclerosis and vascular morphologycharacterization of the portion of the coronary vasculature of thepatient using coronary computed tomographic angiography (CCTA) of theportion of the coronary vasculature of the patient, applying analgorithm that integrates the CFD analysis and the atherosclerosis andvascular morphology characterization to provide an indication of thepresence and/or degree of ischemia within the portion of the coronaryvasculature of the patient on a pixel-by-pixel basis, the algorithmproviding an indication of the presence and/or degree of ischemia for agiven pixel based upon an analysis of the given pixel, the surroundingpixels, and a vessel of the portion of the coronary vasculature of thepatient with which the pixel is associated.

Performing a computational fluid dynamics (CFD) analysis can includegenerating a model of the portion of the coronary vasculature of thepatient based at least in part on coronary computed tomographicangiography (CCTA) of the portion of the coronary vasculature of thepatient. Performing a CFD analysis can include generating a model of theportion of the coronary vasculature of the patient based at least inpart on the atherosclerosis and vascular morphology characterization ofthe portion of the coronary vasculature of the patient. Performing a CFDanalysis can include computing a fractional flow reserve model of theportion of the coronary vasculature of the patient.

Performing a comprehensive atherosclerosis and vascular morphologycharacterization of the portion of the coronary vasculature of thepatient can include determining one or more vascular morphologyparameters and a set of quantified plaque parameters. Performing a CFDanalysis of a portion of the coronary vasculature of a patient caninclude generating a CFD-based indication of the presence and/or degreeof ischemia within the portion of the coronary vasculature of thepatient on a pixel-by-pixel basis. Applying the algorithm thatintegrates the CFD analysis and the atherosclerosis and vascularmorphology characterization to provide an indication of the presenceand/or degree of ischemia within the portion of the coronary vasculatureof the patient on a pixel-by-pixel basis can include providing anindication of agreement with the CFD-based indication of the presenceand/or degree of ischemia within the portion of the coronary vasculatureof the patient on a pixel-by-pixel basis. In some embodiments,information generated from the CFD analysis and information related toone or more vascular morphology parameters and/or a set of quantifiedplaque parameters can be input into a ML algorithm to assess the risk ofCAD or MI. In an example, the ML algorithm compares information from theCFD analysis and/or the information related to one or more vascularmorphology parameters and/or a set of quantified plaque parameters to adatabase of patient information to assess or determine a risk of CAD orMI. In an example, the ML algorithm compares information from the CFDanalysis and/or the information related to one or more vascularmorphology parameters and/or a set of quantified plaque parameters to adatabase of patient information to assess or determine the presenceand/or severity of ischemia. In an example, the ML algorithm can alsouse patient specific information that can include age, gender, race,BMI, medication, blood pressure, heart rate, weight, height, bodyhabitus, smoking, diabetes, hypertension, prior CAD, family history,and/or lab test results to compare CFD and one or more vascularmorphology parameters and/or a set of quantified plaque parameters ofthe patient being evaluated to patients in a database to assess ordetermine the presence and/or severity of ischemia, and/or to assess ordetermine a risk of CAD or MI.

Applying an algorithm that integrates the CFD analysis and theatherosclerosis and vascular morphology characterization to provide anindication of the presence and/or degree of ischemia within the portionof the coronary vasculature of the patient on a pixel-by-pixel basis caninclude analyzing variation in coronary volume, area, and/or diameterover the entirety of a cardiac cycle. Analyzing variation in coronaryvolume, area, and/or diameter over the entirety of a cardiac cycle caninclude analyzing an effect of identified atherosclerotic plaque withina wall of an artery on the deformation of the artery.

In one aspect, a computer implemented method for non-invasivelyestimating blood flow characteristics to assess the severity of plaqueand/or stenotic lesions using contrast distribution predictions andmeasurements is provided, the method including generating and outputtingan initial indicia of a severity of the plaque or stenotic lesion usingone or more calculated blood flow characteristics, where generating andoutputting the initial indicia of a severity of the plaque or stenoticlesion includes receiving one or more patient-specific images and/oranatomical characteristics of at least a portion of a patient’svasculature, receiving images reflecting a measured distribution of acontrast agent delivered through the patient’s vasculature, projectingone or more contrast values of the measured distribution of the contrastagent to one or more points of a patient-specific anatomic model of thepatient’s vasculature generated using the received patient-specificimages and/or the received anatomical thereby creating apatient-specific measured model indicative of the measured distribution,defining one or more physiological and boundary conditions of a bloodflow to non-invasively simulate a distribution of the contrast agentthrough the patient-specific anatomic model of the patient’svasculature, simulating, using a processor, the distribution of thecontrast agent through the one or more points of the patient-specificanatomic model using the defined one or more physiological and boundaryconditions and the received patient-specific images and/or anatomicalcharacteristics, thereby creating a patient-specific simulated modelindicative of the simulated distribution, comparing, using a processor,the patient-specific measured model and the patient-specific simulatedmodel to determine whether a similarity condition is satisfied, updatingthe defined physiological and boundary conditions and re-simulating thedistribution of the contrast agent through the one or more points of thepatient-specific anatomic model until the similarity condition issatisfied, calculating, using a processor, one or more blood flowcharacteristics of blood flow through the patient-specific anatomicmodel using the updated physiological and boundary conditions, andgenerating and outputting the initial indicia of a severity of theplaque or stenotic lesion using the one or more blood flowcharacteristics of blood flow that were calculated using the updatedphysiological and boundary conditions, performing a comprehensiveatherosclerosis and vascular morphology characterization of the portionof the patient’s vasculature using coronary computed tomographicangiography (CCTA) of the portion of the patient’s vasculature, andapplying an algorithm that integrates the initial indicia of a severityof the plaque or stenotic lesion and the atherosclerosis and vascularmorphology characterization to provide an indication of the presenceand/or degree of ischemia within the portion of the patient’svasculature on a pixel-by-pixel basis.

The algorithm can provide an indication of the presence and/or degree ofischemia for a given pixel based upon an analysis of the given pixel,the surrounding pixels, and a vessel of the portion of the coronaryvasculature of the patient with which the pixel is associated. Prior tosimulating the distribution of the contrast agent in thepatient-specific anatomic model for the first time, defining one or morephysiological and boundary conditions can include finding form orfunctional relationships between the vasculature represented by theanatomic model and physiological characteristics found in populations ofpatients with a similar vascular anatomy. Prior to simulating thedistribution of the contrast agent in the patient-specific anatomicmodel for the first time, defining one or more physiological andboundary conditions can include one or more of assigning an initialcontrast distribution, or assigning boundary conditions related to aflux of the contrast agent (i) at one or more of vessel walls, outletboundaries, or inlet boundaries, or (ii) near plaque and/or stenoticlesions.

The blood flow characteristics can include one or more of, a blood flowvelocity, a blood pressure, a heart rate, a fractional flow reserve(FFR) value, a coronary flow reserve (CFR) value, a shear stress, or anaxial plaque stress. Receiving one or more patient-specific images caninclude receiving one or more images from coronary angiography, biplaneangiography, 3D rotational angiography, computed tomography (CT)imaging, magnetic resonance (MR) imaging, ultrasound imaging, or acombination thereof.

The patient-specific anatomic model can be a reduced-order mode in thetwo-dimensional anatomical domain, and projecting the one or morecontrast values can include averaging one or more contrast values overone or more cross sectional areas of a vessel. The patient-specificanatomic model can include information related to the vasculature,including one or more of a geometrical description of a vessel,including the length or diameter, a branching pattern of a vessel, oneor more locations of any stenotic lesions, plaque, occlusions, ordiseased segments, or one or more characteristics of diseases on orwithin vessels, including material properties of stenotic lesions,plaque, occlusions, or diseased segments. The physiological conditionscan be measured, obtained, or derived from computational fluid dynamicsor the patient-specific anatomic model, and can include one or more of,blood pressure flux, blood velocity flux, the flux of the contrastagent, baseline heart rate, geometrical and material characteristics ofthe vasculature, or geometrical and material characteristics of plaqueand/or stenotic lesions, and where the boundary conditions definephysiological relationships between variables at boundaries of a regionof interest, where the boundaries can include one or more of, inflowboundaries, outflow boundaries, vessel wall boundaries, or boundaries ofplaque and/or stenotic lesions.

The simulating, using the processor, of the distribution of the contrastagent for the one or more points in the patient-specific anatomic modelusing the defined one or more physiological and boundary conditions caninclude one or more of determining scalar advection-diffusion equationsgoverning the transport of the contrast agent in the patient-specificanatomic model, the equations governing the transport of the contrastagent reflecting any changes in a ratio of flow to lumen area at or neara stenotic lesion or plaque, or computing a concentration of thecontrast agent for the one or more points of the patient-specificanatomic model, where computing the concentration requires assignment ofan initial contrast distribution and initial physiological and boundaryconditions. Satisfying a similarity condition can include specifying atolerance that can measure differences between the measured distributionof the contrast agent and the simulated distribution of the contrastagent, prior to simulating the distribution of the contrast agent anddetermining whether the difference between the measured distribution ofthe contrast agent and the simulated distribution of the contrast agentfalls within the specified tolerance, the similarity condition beingsatisfied if the difference falls within the specified tolerance.

Updating the defined physiological and boundary conditions andre-simulating the distribution of the contrast agent can include mappinga concentration of the contrast agent along vessels with one or more offeatures derived from an analytic approximation of anadvection-diffusion equation describing the transport of fluid in one ormore vessels of the patient-specific anatomic model, features describingthe geometry of the patient-specific anatomic model, including, one ormore of, a lumen diameter of a plaque or stenotic lesion, a length of asegment afflicted with a plaque or stenotic lesion, a vessel length, orthe area of a plaque or stenotic lesion, or features describing apatient-specific dispersivity of the contrast agent. Updating thedefined physiological and boundary conditions and re-simulating thedistribution of the contrast agent can include using one or more of aderivative-free optimization based on nonlinear ensemble filtering, or agradient-based optimization that uses finite difference or adjointapproximation.

The method can further include, upon a determination that the measureddistribution of the contrast agent and the simulated distribution of thecontrast agent satisfy the similarity condition, enhancing the receivedpatient-specific images using the simulated distribution of the contrastagent, and outputting the enhanced images as one or more medical imagesto an electronic storage medium or display. Enhancing the receivedpatient-specific images can include one or more of replacing pixelvalues with the simulated distribution of the contrast agent, or usingthe simulated distribution of the contrast agent to de-noise thereceived patient-specific images via a conditional random field.

The method can further include, upon a determination that the measureddistribution of the contrast agent and the simulated distribution of thecontrast agent satisfies the similarity condition, using the calculatedblood flow characteristics associated with the simulated distribution ofthe contrast agent to simulate perfusion of blood in one or more areasof the patient-specific anatomic model, generating a model or medicalimage representing the perfusion of blood in one or more areas of thepatient-specific anatomic model, and outputting the model or medicalimage representing the perfusion of blood in one or more areas of thepatient-specific anatomic model to an electronic storage medium ordisplay.

The patient-specific anatomic model can be represented in athree-dimensional anatomical domain, and projecting the one or morecontrast values can include assigning contrast values for each point ofa three-dimensional finite element mesh.

Performing a comprehensive atherosclerosis and vascular morphologycharacterization of the portion of the patient’s vasculature usingcoronary computed tomographic angiography (CCTA) of the portion of thepatient’s vasculature can include generating image information for thepatient, the image information including image data of computedtomography (CT) scans along a vessel of the patient, and radiodensityvalues of coronary plaque and radiodensity values of perivascular tissuelocated adjacent to the coronary plaque, and determining, using theimage information of the patient, coronary plaque information of thepatient, where determining the coronary plaque information can includequantifying, using the image information, radiodensity values in aregion of coronary plaque of the patient, quantifying, using the imageinformation, radiodensity values in a region of perivascular tissueadjacent to the region of coronary plaque of the patient, and generatingmetrics of coronary plaque of the patient using the quantifiedradiodensity values in the region of coronary plaque and the quantifiedradiodensity values in the region of perivascular tissue adjacent to theregion of coronary plaque.

The method can further include accessing a database of coronary plaqueinformation and characteristics of other people, the coronary plaqueinformation in the database including metrics generated fromradiodensity values of a region of coronary plaque in the other peopleand radiodensity values of perivascular tissue adjacent to the region ofcoronary plaque in the other people, and the characteristics of theother people including information at least of age, sex, race, diabetes,smoking, and prior coronary artery disease, and characterizing thecoronary plaque information of the patient by comparing the metrics ofthe coronary plaque information and characteristics of the patient tothe metrics of the coronary plaque information of other people in thedatabase having one or more of the same characteristics, wherecharacterizing the coronary plaque information can include identifyingthe coronary plaque as a high risk plaque. Characterizing the coronaryplaque can include identifying the coronary plaque as a high risk plaqueif it is likely to cause ischemia based on a comparison of the coronaryplaque information and characteristics of the patient to the coronaryplaque information and characteristics of the other people in thedatabase. The characterization of coronary plaque as high risk plaquecan be used to provide an indication of the presence and/or degree ofischemia within a portion of the patient’s vasculature in at least onepixel adjacent the coronary plaque. Characterizing the coronary plaquecan include identifying the coronary plaque as a high risk plaque if itis likely to cause vasospasm based on a comparison of the coronaryplaque information and characteristics of the patient to the coronaryplaque information and characteristics of the other people in thedatabase. Characterizing the coronary plaque can include identifying thecoronary plaque as a high risk plaque if it is likely to rapidlyprogress based on a comparison of the coronary plaque information andcharacteristics of the patient to the coronary plaque information andcharacteristics of the other people in the database.

Generating metrics using the quantified radiodensity values in theregion of coronary plaque and the quantified radiodensity values in aregion of perivascular tissue adjacent to the region of the patient caninclude determining, along a line, a slope value of the radiodensityvalues of the coronary plaque and a slope value of the radiodensityvalues of the perivascular tissue adjacent to the coronary plaque.Generating metrics can further include determining a ratio of the slopevalue of the radiodensity values of the coronary plaque and a slopevalue of the radiodensity values of the perivascular tissue adjacent tothe coronary plaque.

Generating metrics using the quantified radiodensity values in theregion of coronary plaque and the quantified radiodensity values in aregion of perivascular tissue adjacent to the region of the patient caninclude generating, using the image information, a ratio betweenquantified radiodensity values of the coronary plaque and quantifiedradiodensity values of the corresponding perivascular tissue.

The perivascular tissue can be perivascular fat, and generating metricsusing the quantified radiodensity values in the region of coronaryplaque and the quantified radiodensity values in the region ofperivascular tissue adjacent to the region of coronary plaque of thepatient can include generating a ratio of a density of the coronaryplaque and a density of the perivascular fat. The perivascular tissuecan be a coronary artery, and generating metrics using the quantifiedradiodensity values in the region of coronary plaque and the quantifiedradiodensity values in the region of perivascular tissue adjacent to theregion of coronary plaque of the patient can include generating a ratioof a density of the coronary plaque and a density of the coronaryartery. Generating the ratio can include generating the ratio of amaximum radiodensity value of the coronary plaque and a maximumradiodensity value of the perivascular fat. Generating the ratio caninclude generating a ratio of a minimum radiodensity value of thecoronary plaque and a minimum radiodensity value of the perivascularfat. Generating the ratio can include generating a ratio of a maximumradiodensity value of the coronary plaque and a minimum radiodensityvalue of the perivascular fat. Generating the ratio can includegenerating a ratio of a minimum radiodensity value of the coronaryplaque and a maximum radiodensity value of the perivascular fat.

Various examples described elsewhere herein are directed to systems,methods, and devices for medical image analysis, diagnosis, riskstratification, decision making and/or disease tracking. In someembodiments, the systems, devices, and methods described are configuredto utilize non-invasive medical imaging technologies, such as a CT imagefor example, which can be inputted into a computer system configured toautomatically and/or dynamically analyze the medical image to identifyone or more coronary arteries and/or plaque within the same. Forexample, in some embodiments, the system can be configured to utilizeone or more machine learning and/or artificial intelligence algorithmsto automatically and/or dynamically analyze a medical image to identify,quantify, and/or classify one or more coronary arteries and/or plaque.In some embodiments, the system can be further configured to utilize theidentified, quantified, and/or classified one or more coronary arteriesand/or plaque to generate a treatment plan, track disease progression,and/or a patient-specific medical report, for example using one or moreartificial intelligence and/or machine learning algorithms. In someembodiments, the system can be further configured to dynamically and/orautomatically generate a visualization of the identified, quantified,and/or classified one or more coronary arteries and/or plaque, forexample in the form of a graphical user interface. Further, in someembodiments, to calibrate medical images obtained from different medicalimaging scanners and/or different scan parameters or environments, thesystem can be configured to utilize a normalization device comprisingone or more compartments of one or more materials.

As will be discussed in further detail, the systems, devices, andmethods described allow for automatic and/or dynamic quantified analysisof various parameters relating to plaque, cardiovascular arteries,and/or other structures. More specifically, in some embodimentsdescribed herein, a medical image of a patient, such as a coronary CTimage, can be taken at a medical facility. Rather than having aphysician eyeball or make a general assessment of the patient, themedical image is transmitted to a backend main server in someembodiments that is configured to conduct one or more analyses thereofin a reproducible manner. As such, in some embodiments, the systems,methods, and devices described herein can provide a quantifiedmeasurement of one or more features of a coronary CT image usingautomated and/or dynamic processes. For example, in some embodiments,the main server system can be configured to identify one or morevessels, plaque, and/or fat from a medical image. Based on theidentified features, in some embodiments, the system can be configuredto generate one or more quantified measurements from a raw medicalimage, such as for example radiodensity of one or more regions ofplaque, identification of stable plaque and/or unstable plaque, volumesthereof, surface areas thereof, geometric shapes, heterogeneity thereof,and/or the like. In some embodiments, the system can also generate oneor more quantified measurements of vessels from the raw medical image,such as for example diameter, volume, morphology, and/or the like. Basedon the identified features and/or quantified measurements, in someembodiments, the system can be configured to generate a risk assessmentand/or track the progression of a plaque-based disease or condition,such as for example atherosclerosis, stenosis, and/or ischemia, usingraw medical images. Further, in some embodiments, the system can beconfigured to generate a visualization of GUI of one or more identifiedfeatures and/or quantified measurements, such as a quantized colormapping of different features. In some embodiments, the systems,devices, and methods described herein are configured to utilize medicalimage-based processing to assess for a subject his or her risk of acardiovascular event, major adverse cardiovascular event (MACE), rapidplaque progression, and/or non-response to medication. In particular, insome embodiments, the system can be configured to automatically and/ordynamically assess such health risk of a subject by analyzing onlynon-invasively obtained medical images. In some embodiments, one or moreof the processes can be automated using an AI and/or ML algorithm. Insome embodiments, one or more of the processes described herein can beperformed within minutes in a reproducible manner. This is starkcontrast to existing measures today which do not produce reproducibleprognosis or assessment, take extensive amounts of time, and/or requireinvasive procedures.

As such, in some embodiments, the systems, devices, and methodsdescribed are able to provide physicians and/or patients specificquantified and/or measured data relating to a patient’s plaque that donot exist today. For example, in some embodiments, the system canprovide a specific numerical value for the volume of stable and/orunstable plaque, the ratio thereof against the total vessel volume,percentage of stenosis, and/or the like, using for example radiodensityvalues of pixels and/or regions within a medical image. In someembodiments, such detailed level of quantified plaque parameters fromimage processing and downstream analytical results can provide moreaccurate and useful tools for assessing the health and/or risk ofpatients in completely novel ways. Additional information regarding thequantification of detailed plaque data information is described in U.S.Pat. No. 10,813,612 (for example, including but not limited todescription relating to FIGS. 6, 7, and 9-12 ) which is incorporated byreference herein.

The characterization of atherosclerosis and vascular morphology, andother data indicative of the state of the vessels of the patient and thebehavior of those vessels, can be combined with or otherwise used toaugment or improve various types of cardiovascular analysis ormonitoring. By providing a detailed level of quantified plaqueparameters, a more precise patient-specific model can be generated andused in conjunction with computational fluid dynamics (CFD) and/orfluid-structure interaction (FSI) analysis to evaluate patient-specificcoronary pressure and flow.

Overview of Ischemia Identification

Patients with coronary artery disease (CAD) are susceptible to coronaryischemia, in which a coronary vessel exhibits reduced coronary pressureand/or flow. In patients with symptoms suggestive of coronary arterydisease, the identification of coronary ischemia, or the exclusion ofcoronary ischemia, can be helpful in evaluating the coronary arterydisease and determining a recommended treatment. In particular, theidentification of coronary ischemia can indicate a need for invasivetreatment, such as invasive coronary angiography with intended coronaryrevascularization.

Historically, the presence, extent, and severity of ischemia has beendetermined through stress testing. This stress testing can be performedwithout imaging, or can be performed in conjunction with imaging of thepatient. In this stress testing, surrogate or actual measures ofmyocardial blood taken when the patient is at rest are compared tomeasures taken when the patient is in a ‘stress’ states. These stressstates can be achieved through exercise, or can be brought about bypharmacologic vasodilation.

Recently, coronary computed tomographic angiography (CCTA) has beenintroduced as an alternative to stress testing. CCTA allows directvisualization of coronary arteries in a non-invasive fashion. CCTAdemonstrates high diagnostic performance for the detection or exclusionof high-grade coronary legions, such as coronary stenoses where thevessel is abnormally narrowed, which may be the cause of ischemia.

Using diagnostic catheterization, the fractional flow reserve (FFR) ofan observed lesion can be directly measured. Prior studies havedemonstrated a high rate of “false positives” when severe lesions aredetected by CCTA and used as an indicator of coronary ischemia. In suchcases, these detected lesions are not functionally significant, and donot, in fact, cause ischemia by invasive fractional flow reserve.Because these “false positives” may result in the use of invasive andunnecessary procedures for the purposes of confirming and treating theselesions, it is desirable to improve the diagnostic performance ofCCTA-based analysis in the detection of coronary ischemia and otherconditions.

A variety of techniques have been introduced which leverage CCTAfindings for the determination of coronary ischemia. In some techniques,CCTA can be used in conjunction with stress testing. In some techniques,CCTA can be used to calculate a transarterial attenuation gradient,which can be used to determine an estimate of a pressure gradient orfractional flow reserve for the patient. In some techniques,computational fluid dynamics can be applied to CCTA in order to providea three-dimensional evaluation of coronary pressure and/or flow in apatient-specific fashion.

Computational Fluid Dynamics (CFD) Analysis

CFD can be used to evaluate coronary pressure and/or flow for a givenvessel geometry and boundary conditions based on the solving of theNavier-Stokes equations, or similar analysis. This information can beused, for example, to determine the functional significance of acoronary lesion, such as whether the lesion impacts blood flow, and thedegree to which the blood flow is impacted by the lesion. In addition,this information can be used in a predictive manner, such as to predictchanges in coronary blood flow, pressure, or myocardial perfusion underother states such as during exercise or when the patient is otherwiseunder a stress state. This information can also be used to predict theoutcome of treatments or other interventions.

Early CFD-based analysis of the cardiovascular system was used to modelcomplex cerebral vasculature. An overview of the early development ofcomputerized fluid dynamics analysis as applied to the evaluation ofcerebral circulation is described in U.S. Pat. No. 7,191,110, which isincorporated by reference herein in its entirety.

In addition to the fluid dynamics modules that were used to modelvasculature, including cerebral vasculature, electrical models were alsobuilt based on the similarity of the governing equations of electricalcircuits and one-dimensional linear flow, due to the suitability ofelectrical networks for simulating networks with capacitance andresistance. Transmission line equations similar to the linearizedNavier-Stokes equation and vessel wall deformation were used to simulatethe pulsatile flow and flexible vessel wall.

The limitations of computing capacity during early use of CFD-basedanalysis placed significant restrictions on the detail that could beincluded in a practical implementation of CFD-based analysis for a givenpatient. As a result, early CFD-based analysis of portions of thecardiovascular system of a patient included assumptions which simplifiedthe overall model, such as treating the vessel walls as a rigid tube,and treating the blood as a non-compressible Newtonian fluid.

Similar methods were applied to the modeling and evaluation of bloodflow in the coronary arteries and adjacent portions of thecardiovascular system. For example, U.S. Pat. No. 8,386,188, which isincorporated by reference herein in its entirety, describes methods formodeling portions of the cardiovascular system of a patient usingpatient-specific imaging data (for example, including but not limitedto, as described in reference to FIG. 2 ) and generating athree-dimensional model representing at least a portion of the patient’scardiovascular system using the patient-specific data (for example,including but not limited to, as described in reference to FIGS. 3-24 ).

The CFD analysis can be based at least in part on a three-dimensionalmodel of a portion of the cardiovascular system of the patient, such asa portion of the patient’s heart. For example, the three-dimensionalmodel can include the aorta, some or all of the main coronary arteries,and/or other vessels downstream of the main coronary arteries. In someembodiments, the three-dimensional model can include, or can be used togenerate, a volumetric mesh such as a finite element mesh or a finitevolume mesh. In some embodiments, this model can be generated usinginformation obtained from a CCTA, although other imaging techniques,such as magnetic resonance imaging or ultrasound can also be used. Themodel can be dynamic, indicative of the changes in vessel shape over acardiac cycle.

The geometric dimensions of the model can be used to determine theboundary conditions of the vessel walls. In addition, the boundaryconditions at the inlet and the outlet of the section(s) to be analyzedcan also be assigned in any suitable manner, such as by coupling a modelto the boundary. Noninvasive measurements such as cardiac output, bloodpressure, and myocardial mass can be used in assigning the inlet oroutlet boundary conditions. As described in U.S. Pat. No. 7,191,110 andU.S. Pat. No. 8,386,188, reduced order models of portions of thepatient’s vasculature may be generated and used in the CFD analysis, toreduce computing load and to determine boundary conditions for morerobustly modeled portions of the patient’s vasculature.

The CFD analysis can be used to determine blood flow characteristics forthe entire modeled portion of the cardiovascular system of the patient,or for one or more sections within the modeled portion. In someembodiments, the determined blood flow characteristics can include someor all of the blood flow velocity, pressure, flow rate, or FFR atvarious locations throughout the modeled portion of the cardiovascularsystem of the patient. Other conditions and parameters may also becalculated, such as shear stresses throughout the modeled portion of thecardiovascular system of the patient.

The inlet and outlet boundary conditions may be assigned and/or variedbased on a variety of physiologic conditions, including for a state ofrest, or a state of maximum stress or maximum hyperemia, to determineblood flow characteristics under a variety of physiologic conditions.

In some embodiments, a simulated blood pressure model can be generated,where the simulated blood pressure model provides information regardingthe pressure at various locations along the modeled portion of thecardiovascular system of the patient. Such a simulated blood pressuremodel can be used, in turn, to generate an FFR model of the modeledportion of the cardiovascular system of the patient, where the FFR modelcan be calculated as the ratio of the blood pressure at a given locationin the cardiovascular system divided by the blood pressure in the aortaunder conditions of maximum stress, or hyperemia, resulting in increasedcoronary blood flow.

The CFD model may be segmented based upon the geometry of the varioussegments of the modeled portion of the cardiovascular system of thepatient, including both the overall vessel shape and arrangement, aswell as any local variations in geometry. For example, a diseasedportion which has a narrow cross-section, a lesion, and/or a stenosismay be modeled in one or more distinct segments. The cross-sectionalarea and local minimum of the cross-sectional area of the diseasedportions and stenoses may be measured and used in the CFD analysis.

The determined blood flow characteristics, and in particular the localvalues of the calculated FFR model, can be used to provide an indicationof the presence of a functionally significant lesion or other featurewhich may require treatment. In particular, if the calculated FFR at agiven location is below a threshold level, the local drop in FFR isindicative of the presence of a functionally significant lesion locatedupstream of the low FFR point. In some embodiments, an indication of thecalculated FFR throughout the modeled portion of the cardiovascularsystem can be provided as a result, and the location of any functionallysignificant lesions can be identified by a user. In other embodiments,the upstream geometry of the modeled position of the cardiovascularsystem of the patient can be analyzed and the location of anyfunctionally significant lesions can be identified by a computer systemas part of the CFD analysis or as a separate analysis.

U.S. Pat. No. 10,433,740, which is incorporated by reference herein inits entirety, broadly describes an example of machine learning as partof the analysis of a geometric model of a patient in addition to one ormore measured or estimated physiological parameters. The describedparameters may include global parameters, such as blood pressure, bloodviscosity, patient age, patient gender, mass of the supplied tissue, ormay be local, such as an estimated density of the vessel wall at aparticular location. The system described in U.S. Pat. No. 10,433,740,and other similar systems, may create, for each point at which there isa value of a blood flow characteristic, a feature vector describing thepatient specific geometry at that point and estimating of physiologicalor phenotypic parameters of the patient. Such systems as described inU.S. Pat. No. 10,433,740 may train a machine learning algorithm topredict the blood flow characteristics, such as FFR, at the variouspoints from the feature vectors. The system may then, in turn, use theestimate of FFR to classify a vessel or patient as ischemia positive ornegative based on the estimation of FFR.

U.S. Pat. No. 10,307,131, which is incorporated by reference herein inits entirety, describes systems which may utilize more accurateestimations of boundary conditions to improve the accuracy of FFRcomputed tomography used to noninvasively determine FFR. The computedblood flow characteristics may be determined in an iterative fashion, bycomparing a predicted contrast distribution and a measured contrastdistribution until the solution converges, and the computed blood flowcharacteristics may then be used to generate a model used in abiochemical analysis.

However, systems such as those described in U.S. Pat. Nos. 8,386,188,10,433,740, and 10,307,131, are directed primarily to the use ofadditional analysis to improve the accuracy of the calculation of bloodflow characteristics such as FFR, and to use those FFR calculations orestimations to provide more accurate predictions of the functionalseverity of stenoses or the presence of ischemia.

In some embodiments, further analysis may be performed based on a CFDmodel of at least a portion of the cardiovascular system of a patient.In some embodiments, the CFD model described may be updated as describedin U.S. Pat. Nos. 8,386,188, to reflect possible treatments, such as theinsertion of a stent, and the CFD analysis performed based on theupdated model to determine blood flow characteristics for at least aportion of the updated CFD model. Such a system can attempt to reducethe likelihood of a false positive by improving the FFR analysis, butdoes not, for example, provide an independent assessment of the presenceor degree of a condition such as ischemia as a check against potentialfalse positives generated using FFR analysis.

In some embodiments, this CFD analysis can model the coronary arteryand/or other vessels or portions of the cardiovascular system as a rigidtube. In other embodiments, this CFD analysis can model thecardiovascular system as a compliant tube, and the elastodynamicequations for wall dynamics may be solved together with theNavier-Stokes equations. This CFD analysis can model the blood as anon-compressible Newtonian fluid, although the blood may also be modeledas a non-Newtonian or multiphase fluid. In addition, this CFD analysisalso requires certain assumptions in modeling both the boundaryconditions and the vessel behavior, such as coronary vasodilation underhyperemia.

In some embodiments, the model used for the CFD analysis can bedeveloped using or based at least in part on a characterization ofatherosclerosis and vascular morphology as described in U.S. Pat. No.7,191,110. The detail and precision with respect to the atherosclerosisand vascular morphology information which can be determined using thedescribed analysis can increase the accuracy of the CFD analysis by moreprecisely modeling the modeled portion of the cardiovascular system ofthe patient. In some embodiments, the information regardingatherosclerosis and vascular morphology can be used to provide a modelmore indicative of the physical parameters of the modeled portion of thecardiovascular system of the patient, particularly the physicalparameters which are affected by the presence, type, and volume ofplaque.

Similarly, U.S. Pat. No. 10,052,031, which is incorporated by referenceherein in its entirety, describes the computation of hemodynamicqualities indicative of the functional severity of stenosis, which canbe used in the treatment and/or assessment of coronary artery disease.The system can be used to identify lesion specific ischemia using acombination of perfusion scanning data, anatomical imaging of coronaryvessels, and computational fluid dynamics. Like the system described inU.S. Pat. No. 10,307,131, however, the system described in U.S. Pat. No.10,052,031, is directed to improving the computed hemodynamic quantityindicative of the functional severity of the stenosis through iterativecomparison of a simulated perfusion map to a measured perfusion mapobtained by perfusion scanning of a patient.

U.S. Pat. No. 10,888,234, which is incorporated by reference herein inits entirety, describes a system for machine learning based non-invasivefunctional assessment of coronary artery stenosis from medical imagedata. Like other systems in the references incorporated herein, thesystem described in U.S. Pat. No. 10,888,234 is directed towardsimprovement of the determination of an FFR value or other hemodynamicindex value. The system of U.S. Pat. No. 10,888,234, utilizes machinelearning as an alternative to more computationally-intensivephysics-based modeling of portions of the cardiovascular system of thepatient, although mechanistic modeling may also be used to compute anFFR value for used in the analysis.

In some embodiments, Fluid-Surface Interaction (FSI) analysis may beperformed in addition to or in conjunction with the CFD analysis. Thecharacterization of atherosclerosis and vascular morphology provided bythe technology disclosed in U.S. Pat. No. 7,191,110 can allow a moreaccurate model of the portion of the cardiovascular system of thepatient. By modeling the portion of the cardiovascular system of thepatient as a deformable structure, greater accuracy can be obtained inthe output models generated by the CFD analysis.

Atherosclerosis and Vascular Morphology Characterization

In some embodiments, the characterization of atherosclerosis andvascular morphology provided by the technology disclosed in U.S. Pat.No. 7,191,110 can be performed either before or after the performance ofthe CFD analysis discussed above. This process may include taking one ormore medical images of a patient, such as a CCTA, at a medical facility.These images may be transmitted to a backend main server in someembodiments that is configured to conduct one or more analyses thereofin a reproducible manner. This analysis may include the use ofartificial intelligence (AI), machine learning (ML) and/or otheralgorithms. In some embodiments, the systems, methods, and devicesdescribed herein can provide a quantified measurement of one or morefeatures of a coronary CT image using automated and/or dynamicprocesses.

In certain embodiments, the characterization of atherosclerosis andvascular morphology may be performed prior to the performance of the CFDanalysis, and the resulting characterization, or information derivedtherefrom, may be used as part of the generation of a model of a portionof the cardiovascular system of the patient.

In some embodiments, the characterization of atherosclerosis andvascular morphology may include the analysis of a series of CCTA imagesor any other suitable images, and the generation of a three-dimensionalmodel of the patient’s cardiovascular system. This analysis can includethe generation of one or more quantified measurements of vessels fromthe raw medical image, such as for example diameter, volume, morphology,and/or the like. This analysis may segment the vessels in apredetermined manner, or in a dynamic manner, in order to provide moredetailed overview of the vascular morphology of the patient.

In particular, in some embodiments, the system can be configured toutilize one or more AI and/or ML algorithms to automatically and/ordynamically identify one or more arteries, including for examplecoronary arteries, carotid arteries, aorta, renal artery, lowerextremity artery, and/or cerebral artery. In some embodiments, one ormore AI and/or ML algorithms use a neural network (CNN) that is trainedwith a set of medical images (e.g., CT scans) on which arteries andfeatures (e.g., plaque, lumen, perivascular tissue, and/or vessel walls)have been identified, thereby allowing the AI and/or ML algorithm toautomatically identify arteries directly from a medical image. In someembodiments, the arteries are identified by size and/or location.

This analysis can also include the identification and classification ofplaque within the cardiovascular system of the patient. In someembodiments, the system can be configured to identify a vessel wall anda lumen wall for each of the identified coronary arteries in the medicalimage. In some embodiments, the system is then configured to determinethe volume in between the vessel wall and the lumen wall as plaque. Insome embodiments, the system can be configured to identify regions ofplaque based on the radiodensity values typically associated withplaque, for example by setting a predetermined threshold or range ofradiodensity values that are typically associated with plaque with orwithout normalizing using a normalization device.

In some embodiments, the characterization of atherosclerosis may includethe generation of one or more quantified measurements from a raw medicalimage, such as for example radiodensity of one or more regions ofplaque, identification of stable plaque and/or unstable plaque, volumesthereof, surface areas thereof, geometric shapes, heterogeneity thereof,and/or the like. Using this plaque identification and classification,the overall plaque volume may be determined, as well as the amount ofcalcified stable plaque and the amount of uncalcified plaque. In someembodiments, more detailed classification of atherosclerosis than abinary assessment of calcified vs. non-calcified plaque may be made. Forexample, the plaque may be classified ordinally, with plaque classifiedas dense calcified plaque, calcified plaque, fibrous plaque, fibrofattyplaque, necrotic core, or admixtures of plaque types. The plaque mayalso be classified continuously, by attenuation density on a scale suchas a Hounsfield unit scale or a similar classification system.

The information which can be obtained in the characterization ofatherosclerosis may be dependent upon the type of imaging beingperformed. For example, when the CCTA images are creating using asingle-energy CT process, the relative material density of the plaquerelative to the surrounding tissue can be determined, but the absolutematerial density may be unknown. In contrast, when the CCTA images arecreating using a multi-energy CT process, the absolute material densityof the plaque and other surrounding tissue can be measured.

The characterization of atherosclerosis and vascular morphology mayinclude in particular the identification and classification of stenoseswithin the cardiovascular system of the patient. This may include thecalculation or determination of a numerical calculation orrepresentation of coronary stenosis based on the quantified and/orclassified atherosclerosis derived from the medical image. The systemmay be configured to calculate stenosis using the one or more vascularmorphology parameters and/or quantified plaque parameters derived fromthe medical image of a coronary region of the patient. In someembodiments, the system is configured to dynamically identify an area ofstenosis within an artery, and calculate information regarding the areaof stenosis, such as vessel parameters including diameter, curvature,local vascular morphology, and the shape of the vessel wall and thelumen wall in the area of stenosis.

The identified stenoses may be used in the generation of a model of aportion of the cardiovascular system of the patient. The use of thequantified stenosis information may include the modeling of the vesselboundary conditions. The use of the quantified stenosis information mayalso include the use of the quantified stenosis information to determinea segmentation of the model for use in the CFD analysis or subsequentprocessing, or to alter the relative density of the nodes of athree-dimensional mesh used as a CFD model, with increased node densityat and around identified stenoses.

In an embodiment in which the CFD analysis is primarily focused on theidentification of functionally significant stenoses, providingadditional detail in a calculated FFR in regions expected to be ofparticular interest can improve the CFD analysis while withoutsignificantly increasing the overall computational load. This may be ofparticular utility when the CFD analysis is augmented or replaced with amore computationally-intensive FSI analysis of at least a portion of themodeled portion of the cardiovascular system of the patient.

In other embodiments, the CFD modeling and CFD analysis may be performedpartially or wholly independent of the characterization ofatherosclerosis and vascular morphology. In such an embodiment, the CFDmodeling and analysis may be performed prior to, or in parallel with,the characterization of atherosclerosis and vascular morphology.

CFD Analysis Verification Using Atherosclerosis and Vascular MorphologyCharacterization

In addition to the identification of functionally significant stenosesor legions using CFD analysis, the characterization of atherosclerosisand vascular morphology can be used to provide an independent assessmentof functionally significant stenoses and whether a given vessel isischemic.

In particular, the determined data and calculations resulting from theatherosclerosis characterization can be analyzed to detectcharacteristics of atherosclerosis and vascular morphology whichincrease the likelihood of a vessel being ischemic. Thesecharacteristics indicative of vessel ischemia include, but are notlimited to, the presence and/or volume of non-calcified plaque, and inparticular low-density non-calcified plaque. Other characteristics whichcan be analyzed to provide an indication of vessel ischemia includelumen volume and positive remodeling of vessels in the area of lesionsor stenoses.

The analysis of these characteristics can be combined with the CFDanalysis to improve the discrimination of vessels as ischemic or notischemic. The analysis of these characteristics can also be used toaugment the information used to generate the CFD model and perform theCFD analysis.

Because CCTA images can be acquired over the entire cardiac cycle,differences in coronary volume, area, and/or diameter can be observedand measured as the coronary arteries dilate and/or constrict. Therelationship of the atherosclerotic plaque within the wall of theartery, coupled to its relative ability to dilate and/or constrict, canalso provide information on the effects of the atherosclerotic plaque onnormal coronary vasomotor function, Even when the absolute materialdensity of the identified plaque is unknown, such as due to the use of asingle-energy CT process to acquire the CCTA, information regarding thestructural properties of the identified plaque can be determined byobservation of the ability of a given portion of a vessel to dilateand/or constrict in comparison to surrounding portions of the vessel.

In addition to analyzing variance in vessel dimensions over the courseof a cardiac cycle, the physiologic condition of the patient may varyover the course of a CCTA acquisition process. For example,nitroglycerin may often be administered immediately before CCFAacquisition, and may also be administered after non-contrast CCTAacquisitions. Because both nitroglycerin and iodinated contrast areknown to have vasodilatory properties, the coronary lumen value willincrease after administration, to a volume larger than the coronarylumen value in the absence of administration.

Nitroglycerin-dependent coronary vasodilation is anendothelial-dependent process. Because ischemia is preceded byendothelial dysfunction, areas of non-dilation may be an anatomic /physiologic indicator of coronary health. Areas of non-dilation may beidentified, such as by comparison of the vascular morphologypre-dilation and post-dilation, and can be analyzed in conjunction withthe atherosclerosis characterization of the plaque in the identifiedareas of poor dilation.

FIG. 26 is a flowchart illustrating a process 2600 for analyzing aCFD-based indication of ischemia using a characterization ofatherosclerosis and vascular morphology. At block 2605, a system canaccess a plurality of images obtained of a patient at a medicalfacility. These images can be CCTA images or any other suitable imagesgenerated using any other suitable imaging method discussed herein or inthe attached appendices. These images can be reflective of a portion ofthe cardiovascular system of a patient, and can be representative of atleast an entire cardiac cycle. In some embodiments, these CCTA imagesmay be reflective of the portion of the cardiovascular system of apatient both prior to and after exposure of the patient to avasodilatory substance, such as nitroglycerin or iodinated contrast.These CCTA images can be reflective of one or more known physiologiccondition of the patient, such as an at rest state or a hyperemic state.

At block 2610, the system can perform a computational fluid dynamics(CFD) analysis based on the plurality of CCTA images. This CFD analysiscan include the evaluation of the CCTA images to generate a model of aportion of the cardiovascular system of a patient shown in the images,and can include the generation of a three-dimensional mesh. This CFDanalysis can include assigning boundary conditions to the CFD modelindicative of the input flow and output flow(s) at the edges of themodeled portion of the cardiovascular system. These boundary conditionscan be assigned at least in part on the basis of non-invasivemeasurements of the patient, such as myocardial mass, cardiac output,and blood pressure. These boundary conditions can also be assigned basedon an analysis of the CCTA images,

This CFD analysis may result in the determination of blood flowcharacteristics of some or all of the modeled portion of thecardiovascular system of the patient. In some embodiments, thedetermined blood flow characteristics can include some or all of theblood flow velocity, pressure, or flow rate at various locationsthroughout the modeled portion of the cardiovascular system of thepatient. In addition, models may be calculated based on the determinedblood flow characteristics, such as an FFR model indicative of FFR atvarious locations throughout the modeled portion of the cardiovascularsystem of the patient, or shear stresses throughout the modeled portionof the cardiovascular system of the patient.

At block 2615, the system can determine a CFD-based indication ofischemia-causing stenosis based on the CFD analysis. This CFD-basedindication of ischemia-causing stenosis may include, for example, thecomparison of an FFR model to a predetermined threshold to identifyregions of the FFR model at which the calculated FFR is below thethreshold. Such an area of low FFR is indicative of a functionallysignificant lesion or stenosis upstream of the low FFR area, and can beused to identify a severe stenosis or otherwise diseased portion of ablood vessel as likely causing the vessel to be ischemic.

At block 2620, the system can determine a characterization ofatherosclerosis and vascular morphology based on a plurality of CCTAimages. These CCTA images may be the images used to perform the CFDanalysis, or may be a different set of images. The characterization ofatherosclerosis can include the identification of the location, volumeand/or type of plaque throughout the portion of the cardiovascularsystem of the patient. In some embodiments, the determination of thecharacterization of atherosclerosis and vascular morphology can bedetermined prior to the CFD analysis, and at least some of thedetermined information can be used as part of the CFD analysis, such asin generating a geometric model of the portion of the cardiovascularsystem of the patient.

At block 2525, the system can apply an algorithm that integrates the CFDanalysis and the characterization of atherosclerosis and vascularmorphology to provide an indication of the presence and/or degree ofischemia within the portion of the coronary vasculature of the patienton a pixel-by-pixel basis. For example, the algorithm may map both theCFD-based indication of ischemia-causing stenosis and thecharacterization of atherosclerosis and vascular morphology to a commonimage. As a result, some or all of the pixels in the vessels of theanalyzed portion of the cardiovascular system of the patient can bedesignated as depicting or not depicting a functionally significantischemia-causing stenosis.

In some embodiments, only certain of the pixels of the blood vessels maybe assigned such an indication. For example, rather than assigning anegative indication to certain pixels, the pixels depicting afunctionally significant ischemia-causing stenosis, or a representativesubset thereof, may be designated with such an indicator, while otherpixels which do not depict a functionally significant ischemia-causingstenosis are not assigned an indication.

The algorithm may make a determination, on a pixel-by-pixel basis, ofthe accuracy of the CFD-based indication of the presence of anischemia-causing stenosis. This determination may be made, for example,by analyzing characteristics of the characterized atherosclerosis andvascular morphology mapped to that pixel and adjacent pixels, such asthose mapped to a common vessel. A determination can be made as towhether those characteristics are consistent with the likelihood of theassociated vessel to be ischemic.

Depending on the data available from the CCTA, additional comparisonsmay be made as part of this determination. For example, where the CCTAis reflective of at least one complete cardiac cycle, the relativeability of a portion of a vessel wall to dilate and/or constrict can beanalyzed in conjunction with the atherosclerosis characterization toprovide information on the effects of the atherosclerotic plaque onnormal coronary vasomotor function. As another example, where the CCTAis reflective of the cardiovascular system of the patient both beforeand after exposure to a vasodilating substance, the CCTA images can becompared to identify areas of non-dilation or other features, responses,or behaviors indicative of endothelial dysfunction.

In some embodiments, the algorithm may make a binary yes/nodetermination as to whether the CFD-based indication of the presence ofan ischemia-causing stenosis is accurate. In other embodiments, one orboth of the CFD-based indication of the presence of an ischemia-causingstenosis and the algorithmic determination of agreement with thatCFD-based determination may not be a binary yes/no decision. In someparticular embodiments, one or both of the CFD-based indication of thepresence of an ischemia-causing stenosis and the algorithmicdetermination of agreement with that CFD-based determination may be aprobability assigned to a given pixel, or a probabilistic modelingapplied to all of the pixels that comprise a given vessel, and to all ofthe pixels that comprise the analyzed portion of the cardiovascularsystem of the patient.

In some embodiments, the algorithm may apply this analysis only to thosepixels for which the CFD analysis indicated the presence of anischemia-causing stenosis, such that the analysis of thecharacterization of atherosclerosis and vascular morphology is performedonly to filter out potential false positives. In other embodiments,however, the algorithm may apply this analysis to some or all of thepixels for which there is no indication of the presence of anischemia-causing stenosis, to identify potentially ischemic vesselswhich might not be identified by the CFD analysis.

Although described primarily with respect to coronary vessels, thedisclosed technology may be used in the analysis of other vesselselsewhere in the body of a patient.

Examples of Embodiments Relating to Combining CFD-Based Evaluation WithAtherosclerosis and Vascular Morphology

The following are non-limiting examples of certain embodiments ofsystems and methods for CFD-based evaluation with atherosclerosis andvascular morphology and/or other related features. Other embodiments mayinclude one or more other features, or different features, that arediscussed herein. Various embodiments described herein relate tosystems, methods, and devices for medical image analysis, diagnosis,risk stratification, decision making and/or disease tracking. In theembodiments illustrated below, in some examples of other embodiments,instead of being performed on a “pixel” or a pixel-by-pixel basis asindicated, the embodiments relate to analysis per lesion, stenosis, persegment, per vessel, and/or per patient, that is, on a lesion-by-lesionbasis, a stenosis-by-stenosis basis, a segment-by-segment basis, avessel-by-vessel basis, or a patient-by-patient basis.

Embodiment 1: A computer-implemented method of identifying a presenceand/or degree of ischemia via an algorithm-based medical imaginganalysis, comprising: performing a computational fluid dynamics (CFD)analysis of a portion of the coronary vasculature of a patient usingimaging data of the portion of the coronary vasculature of the patient;performing a comprehensive atherosclerosis and vascular morphologycharacterization of the portion of the coronary vasculature of thepatient using coronary computed tomographic angiography (CCTA) of theportion of the coronary vasculature of the patient; and applying analgorithm that integrates the CFD analysis and the atherosclerosis andvascular morphology characterization to provide an indication of thepresence and/or degree of ischemia within the portion of the coronaryvasculature of the patient on a pixel-by-pixel basis, the algorithmproviding an indication of the presence and/or degree of ischemia for agiven pixel based upon an analysis of the given pixel, the surroundingpixels, and a vessel of the portion of the coronary vasculature of thepatient with which the pixel is associated. In other examples, insteadof and/or in addition to a pixel-by-pixel basis, an indication of thepresence and/or degree of ischemia within the portion of the coronaryvasculature of the patient can be on a lesion-by-lesion basis, astenosis-by-stenosis basis, a segment-by-segment basis, avessel-by-vessel basis, or a patient-by-patient basis.

Embodiment 2: The method of embodiment 1, wherein performing acomputational fluid dynamics (CFD) analysis comprises generating a modelof the portion of the coronary vasculature of the patient based at leastin part on coronary computed tomographic angiography (CCTA) of theportion of the coronary vasculature of the patient.

Embodiment 3: The method of embodiment 1, wherein performing acomputational fluid dynamics (CFD) analysis comprises generating a modelof the portion of the coronary vasculature of the patient based at leastin part on the atherosclerosis and vascular morphology characterizationof the portion of the coronary vasculature of the patient.

Embodiment 4: The method of embodiment 1, wherein performing acomputational fluid dynamics (CFD) analysis comprises computing afractional flow reserve model of the portion of the coronary vasculatureof the patient.

Embodiment 5: The method of embodiment 1, wherein performing acomprehensive atherosclerosis and vascular morphology characterizationof the portion of the coronary vasculature of the patient comprisesdetermining one or more vascular morphology parameters and a set ofquantified plaque parameters.

Embodiment 6: The method of embodiment 1, wherein performing acomputational fluid dynamics (CFD) analysis of a portion of the coronaryvasculature of a patient comprises (i) generating a CFD-based indicationof the presence and/or degree of ischemia within the portion of thecoronary vasculature of the patient on a pixel-by-pixel basis, (and/oron a lesion-by-lesion basis, a stenosis-by-stenosis basis, asegment-by-segment basis, a vessel-by-vessel basis, or apatient-by-patient basis).

Embodiment 7: The method of any one of embodiments 1-6, wherein applyingthe algorithm that integrates the CFD analysis and the atherosclerosisand vascular morphology characterization to provide an indication of thepresence and/or degree of ischemia within the portion of the coronaryvasculature of the patient on a pixel-by-pixel basis comprises providingan indication of agreement with the CFD-based indication of the presenceand/or degree of ischemia within the portion of the coronary vasculatureof the patient on a pixel-by-pixel basis. Instead of, or in addition to,a pixel-by-pixel basis, this process can be performed on alesion-by-lesion basis, a stenosis-by-stenosis basis, asegment-by-segment basis, a vessel-by-vessel basis, or apatient-by-patient basis.

Embodiment 8: The method of any one of embodiments 1-7, wherein applyingthe algorithm that integrates the CFD analysis and the atherosclerosisand vascular morphology characterization to provide an indication of thepresence and/or degree of ischemia within the portion of the coronaryvasculature of the patient on a pixel-by-pixel basis comprises analyzingvariation in coronary volume, area, and/or diameter over the entirety ofa cardiac cycle. Instead of, or in addition to, a pixel-by-pixel basis,this process can be performed on a lesion-by-lesion basis, astenosis-by-stenosis basis, a segment-by-segment basis, avessel-by-vessel basis, or a patient-by-patient basis.

Embodiment 9: The method of embodiment 8, wherein analyzing variation incoronary volume, area, and/or diameter over the entirety of a cardiaccycle comprises analyzing an effect of identified atherosclerotic plaquewithin a wall of an artery on the deformation of the artery.

Embodiment 10: A computer implemented method for non-invasivelyestimating blood flow characteristics to assess the severity of plaqueand/or stenotic lesions using blood flow predictions and measurements,the method comprising: generating and outputting an initial indicia of aseverity of the plaque or stenotic lesion using one or more calculatedblood flow characteristics, where generating and outputting the initialindicia of a severity of the plaque or stenotic lesion comprises:receiving one or more patient-specific images and/or anatomicalcharacteristics of at least a portion of a patient’s vasculature;receiving images reflecting a measured blood distribution the patient’svasculature; projecting one or more values of the measured distributionto one or more points of a patient-specific anatomic model of thepatient’s vasculature generated using the received patient-specificimages and/or the received anatomical thereby creating apatient-specific measured model indicative of the measured distribution;defining one or more physiological and boundary conditions of a bloodflow to non-invasively simulate a distribution of the blood flow throughthe patient-specific anatomic model of the patient’s vasculature;simulating, using a processor, the distribution of the blood flowthrough the one or more points of the patient-specific anatomic modelusing the defined one or more physiological and boundary conditions andthe received patient-specific images and/or anatomical characteristics,thereby creating a patient-specific simulated model indicative of thesimulated distribution; comparing, using a processor, thepatient-specific measured model and the patient-specific simulated modelto determine whether a similarity condition is satisfied; updating thedefined physiological and boundary conditions and re-simulating thedistribution of the blood flow through the one or more points of thepatient-specific anatomic model until the similarity condition issatisfied; calculating, using a processor, one or more blood flowcharacteristics of blood flow through the patient-specific anatomicmodel using the updated physiological and boundary conditions; andgenerating and outputting the initial indicia of a severity of theplaque or stenotic lesion using the one or more blood flowcharacteristics of blood flow that were calculated using the updatedphysiological and boundary conditions; performing a comprehensiveatherosclerosis and vascular morphology characterization of the portionof the patient’s vasculature using coronary computed tomographicangiography (CCTA) of the portion of the patient’s vasculature; andapplying an algorithm that integrates the initial indicia of a severityof the plaque or stenotic lesion and the atherosclerosis and vascularmorphology characterization to provide an indication of the presenceand/or degree of ischemia within the portion of the patient’svasculature on a pixel-by-pixel basis.

Alternate Embodiment 10 using contrast agent (note: any of theembodiments listed below that refer to “Embodiment 10” or referenceEmbodiment 10 are intended to be practiced with Embodiment 10 and/orAlternate Embodiment 10): A computer implemented method fornon-invasively estimating blood flow characteristics to assess theseverity of plaque and/or stenotic lesions using contrast distributionpredictions and measurements, the method comprising: generating andoutputting an initial indicia of a severity of the plaque or stenoticlesion using one or more calculated blood flow characteristics, wheregenerating and outputting the initial indicia of a severity of theplaque or stenotic lesion comprises: receiving one or morepatient-specific images and/or anatomical characteristics of at least aportion of a patient’s vasculature; receiving images reflecting ameasured distribution of a contrast agent delivered through thepatient’s vasculature; projecting one or more contrast values of themeasured distribution of the contrast agent to one or more points of apatient-specific anatomic model of the patient’s vasculature generatedusing the received patient-specific images and/or the receivedanatomical thereby creating a patient-specific measured model indicativeof the measured distribution; defining one or more physiological andboundary conditions of a blood flow to non-invasively simulate adistribution of the contrast agent through the patient-specific anatomicmodel of the patient’s vasculature; simulating, using a processor, thedistribution of the contrast agent through the one or more points of thepatient-specific anatomic model using the defined one or morephysiological and boundary conditions and the received patient-specificimages and/or anatomical characteristics, thereby creating apatient-specific simulated model indicative of the simulateddistribution; comparing, using a processor, the patient-specificmeasured model and the patient-specific simulated model to determinewhether a similarity condition is satisfied; updating the definedphysiological and boundary conditions and re-simulating the distributionof the contrast agent through the one or more points of thepatient-specific anatomic model until the similarity condition issatisfied; calculating, using a processor, one or more blood flowcharacteristics of blood flow through the patient-specific anatomicmodel using the updated physiological and boundary conditions; andgenerating and outputting the initial indicia of a severity of theplaque or stenotic lesion using the one or more blood flowcharacteristics of blood flow that were calculated using the updatedphysiological and boundary conditions; performing a comprehensiveatherosclerosis and vascular morphology characterization of the portionof the patient’s vasculature using coronary computed tomographicangiography (CCTA) of the portion of the patient’s vasculature; andapplying an algorithm that integrates the initial indicia of a severityof the plaque or stenotic lesion and the atherosclerosis and vascularmorphology characterization to provide an indication of the presenceand/or degree of ischemia within the portion of the patient’svasculature on a pixel-by-pixel basis.

Embodiment 11: The method of embodiment 10, wherein the algorithmprovides an indication of the presence and/or degree of ischemia for agiven pixel based upon an analysis of the given pixel, the surroundingpixels, and a vessel of the portion of the coronary vasculature of thepatient with which the pixel is associated. Instead of, or in additionto, a pixel basis, this process can be performed on a lesion basis, astenosis basis, a segment basis, a vessel basis, or a patient basis.

Embodiment 12: The computer method of embodiments 10 or 11, wherein,prior to simulating the distribution of the contrast agent in thepatient-specific anatomic model for the first time, defining one or morephysiological and boundary conditions includes finding form orfunctional relationships between the vasculature represented by theanatomic model and physiological characteristics found in populations ofpatients with a similar vascular anatomy.

Embodiment 13: The method of embodiments 10 or 11, wherein, prior tosimulating the distribution of the contrast agent in thepatient-specific anatomic model for the first time, defining one or morephysiological and boundary conditions includes, one or more of:assigning an initial contrast distribution; or assigning boundaryconditions related to a flux of the contrast agent (i) at one or more ofvessel walls, outlet boundaries, or inlet boundaries, or (ii) nearplaque and/or stenotic lesions.

Embodiment 14: The method of any one of embodiments 10-13, wherein theblood flow characteristics include one or more of, a blood flowvelocity, a blood pressure, a heart rate, a fractional flow reserve(FFR) value, a coronary flow reserve (CFR) value, a shear stress, or anaxial plaque stress.

Embodiment 15: The method of any one of embodiments 10-14, whereinreceiving one or more patient-specific images includes receiving one ormore images from coronary angiography, biplane angiography, 3Drotational angiography, computed tomography (CT) imaging, magneticresonance (MR) imaging, ultrasound imaging, or a combination thereof.

Embodiment 16: The method of any one of embodiments 10-15, wherein thepatient-specific anatomic model is a reduced-order mode in the two-dimensional anatomical domain, and wherein projecting the one or morecontrast values includes averaging one or more contrast values over oneor more cross sectional areas of a vessel.

Embodiment 17: The method of any one of embodiments 10-16, wherein thepatient-specific anatomic model includes information related to thevasculature, including one or more of: a geometrical description of avessel, including the length or diameter; a branching pattern of avessel; one or more locations of any stenotic lesions, plaque,occlusions, or diseased segments; or one or more characteristics ofdiseases on or within vessels, including material properties of stenoticlesions, plaque, occlusions, or diseased segments.

Embodiment 18: The method of any one of embodiments 10-17, wherein thephysiological conditions are measured, obtained, or derived fromcomputational fluid dynamics or the patient-specific anatomic model,including, one or more of, blood pressure flux, blood velocity flux, theflux of the contrast agent, baseline heart rate, geometrical andmaterial characteristics of the vasculature, or geometrical and materialcharacteristics of plaque and/or stenotic lesions; and wherein theboundary conditions define physiological relationships between variablesat boundaries of a region of interest, the boundaries including, one ormore of, inflow boundaries, outflow boundaries, vessel wall boundaries,or boundaries of plaque and/or stenotic lesions.

Embodiment 19: The method of any one of embodiments 10-18, whereinsimulating, using the processor, the distribution of the contrast agentfor the one or more points in the patient-specific anatomic model usingthe defined one or more physiological and boundary conditions includesone or more of: determining scalar advection-diffusion equationsgoverning the transport of the contrast agent in the patient-specificanatomic model, the equations governing the transport of the contrastagent reflecting any changes in a ratio of flow to lumen area at or neara stenotic lesion or plaque; or computing a concentration of thecontrast agent for the one or more points of the patient-specificanatomic model, wherein computing the concentration requires assignmentof an initial contrast distribution and initial physiological andboundary conditions.

Embodiment 20: The method of any one of embodiments 10-19, whereinsatisfying a similarity condition comprises: specifying a tolerance thatcan measure differences between the measured distribution of thecontrast agent and the simulated distribution of the contrast agent,prior to simulating the distribution of the contrast agent; anddetermining whether the difference between the measured distribution ofthe contrast agent and the simulated distribution of the contrast agentfalls within the specified tolerance, the similarity condition beingsatisfied if the difference falls within the specified tolerance.

Embodiment 21: The method of any one of embodiments 10-20, whereinupdating the defined physiological and boundary conditions andre-simulating the distribution of the contrast agent includes mapping aconcentration of the contrast agent along vessels with one or more of:features derived from an analytic approximation of anadvection-diffusion equation describing the transport of fluid in one ormore vessels of the patient-specific anatomic model; features describingthe geometry of the patient-specific anatomic model, including, one ormore of, a lumen diameter of a plaque or stenotic lesion, a length of asegment afflicted with a plaque or stenotic lesion, a vessel length, orthe area of a plaque or stenotic lesion; or features describing apatient-specific dispersivity of the contrast agent.

Embodiment 22: The method of any one of embodiments 10-21, whereinupdating the defined physiological and boundary conditions andre-simulating the distribution of the contrast agent includes using oneor more of a derivative-free optimization based on nonlinear ensemblefiltering, or a gradient-based optimization that uses finite differenceor adjoint approximation.

Embodiment 23: The method of any one of embodiments 10-22, furthercomprising: upon a determination that the measured distribution of thecontrast agent and the simulated distribution of the contrast agentsatisfy the similarity condition, enhancing the receivedpatient-specific images using the simulated distribution of the contrastagent; and outputting the enhanced images as one or more medical imagesto an electronic storage medium or display.

Embodiment 24: The method of embodiment 23, wherein enhancing thereceived patient-specific images comprises one or more of: replacingpixel values with the simulated distribution of the contrast agent; orusing the simulated distribution of the contrast agent to de-noise thereceived patient-specific images via a conditional random field.

Embodiment 25: The method of any one of embodiments 10-24, furthercomprising: upon a determination that the measured distribution of thecontrast agent and the simulated distribution of the contrast agentsatisfies the similarity condition, using the calculated blood flowcharacteristics associated with the simulated distribution of thecontrast agent to simulate perfusion of blood in one or more areas ofthe patient-specific anatomic model; generating a model or medical imagerepresenting the perfusion of blood in one or more areas of thepatient-specific anatomic model; and outputting the model or medicalimage representing the perfusion of blood in one or more areas of thepatient-specific anatomic model to an electronic storage medium ordisplay.

Embodiment 26: The method of any one of embodiments 10-25, wherein thepatient-specific anatomic model is represented in a three-dimensionalanatomical domain, and wherein projecting the one or more contrastvalues includes assigning contrast values for each point of athree-dimensional finite element mesh.

Embodiment 27: The method of any one of embodiments 10-26, whereinperforming a comprehensive atherosclerosis and vascular morphologycharacterization of the portion of the patient’s vasculature usingcoronary computed tomographic angiography (CCTA) of the portion of thepatient’s vasculature comprises: generating image information for thepatient, the image information including image data of computedtomography (CT) scans along a vessel of the patient, and radiodensityvalues of coronary plaque and radiodensity values of perivascular tissuelocated adjacent to the coronary plaque; and determining, using theimage information of the patient, coronary plaque information of thepatient, wherein determining the coronary plaque information comprisesquantifying, using the image information, radiodensity values in aregion of coronary plaque of the patient, quantifying, using the imageinformation, radiodensity values in a region of perivascular tissueadjacent to the region of coronary plaque of the patient, and generatingmetrics of coronary plaque of the patient using the quantifiedradiodensity values in the region of coronary plaque and the quantifiedradiodensity values in the region of perivascular tissue adjacent to theregion of coronary plaque.

Embodiment 28: The method of embodiment 27, further comprising:accessing a database of coronary plaque information and characteristicsof other people, the coronary plaque information in the databaseincluding metrics generated from radiodensity values of a region ofcoronary plaque in the other people and radiodensity values ofperivascular tissue adjacent to the region of coronary plaque in theother people, and the characteristics of the other people includinginformation at least of age, sex, race, diabetes, smoking, and priorcoronary artery disease; and characterizing the coronary plaqueinformation of the patient by comparing the metrics of the coronaryplaque information and characteristics of the patient to the metrics ofthe coronary plaque information of other people in the database havingone or more of the same characteristics, wherein characterizing thecoronary plaque information includes identifying the coronary plaque asa high risk plaque.

Embodiment 29: The method of embodiment 28, wherein characterizing thecoronary plaque comprises identifying the coronary plaque as a high riskplaque if it is likely to cause ischemia based on a comparison of thecoronary plaque information and characteristics of the patient to thecoronary plaque information and characteristics of the other people inthe database.

Embodiment 30: The method of embodiment 29, wherein the characterizationof coronary plaque as high risk plaque is used to provide an indicationof the presence and/or degree of ischemia within a portion of thepatient’s vasculature in at least one pixel adjacent the coronaryplaque.

Embodiment 31: The method of embodiment 28, wherein characterizing thecoronary plaque comprises identifying the coronary plaque as a high riskplaque if it is likely to cause vasospasm based on a comparison of thecoronary plaque information and characteristics of the patient to thecoronary plaque information and characteristics of the other people inthe database.

Embodiment 32: The method of embodiment 28, wherein characterizing thecoronary plaque comprises identifying the coronary plaque as a high riskplaque if it is likely to rapidly progress based on a comparison of thecoronary plaque information and characteristics of the patient to thecoronary plaque information and characteristics of the other people inthe database.

Embodiment 33: The method of any one of embodiments 10-32, whereingenerating metrics using the quantified radiodensity values in theregion of coronary plaque and the quantified radiodensity values in aregion of perivascular tissue adjacent to the region of the patientcomprises determining, along a line, a slope value of the radiodensityvalues of the coronary plaque and a slope value of the radiodensityvalues of the perivascular tissue adjacent to the coronary plaque.

Embodiment 34: The method of embodiment 33, wherein generating metricsfurther comprises determining a ratio of the slope value of theradiodensity values of the coronary plaque and a slope value of theradiodensity values of the perivascular tissue adjacent to the coronaryplaque.

Embodiment 35: The method of any one of embodiments 10-34, whereingenerating metrics using the quantified radiodensity values in theregion of coronary plaque and the quantified radiodensity values in aregion of perivascular tissue adjacent to the region of the patientcomprises generating, using the image information, a ratio betweenquantified radiodensity values of the coronary plaque and quantifiedradiodensity values of the corresponding perivascular tissue.

Embodiment 36: The method of any one of embodiments 10-35, wherein theperivascular tissue is perivascular fat, and generating metrics usingthe quantified radiodensity values in the region of coronary plaque andthe quantified radiodensity values in the region of perivascular tissueadjacent to the region of coronary plaque of the patient comprisesgenerating a ratio of a density of the coronary plaque and a density ofthe perivascular fat.

Embodiment 37: The method of any one of embodiments 10-35, wherein theperivascular tissue is a coronary artery, and generating metrics usingthe quantified radiodensity values in the region of coronary plaque andthe quantified radiodensity values in the region of perivascular tissueadjacent to the region of coronary plaque of the patient comprisesgenerating a ratio of a density of the coronary plaque and a density ofthe coronary artery.

Embodiment 38: The method of embodiment 37, wherein generating the ratiocomprises generating the ratio of a maximum radiodensity value of thecoronary plaque and a maximum radiodensity value of the perivascularfat.

Embodiment 39: The method of embodiment 37, wherein generating the ratiocomprises generating a ratio of a minimum radiodensity value of thecoronary plaque and a minimum radiodensity value of the perivascularfat.

Embodiment 40: The method of embodiment 37, wherein generating the ratiocomprises generating a ratio of a maximum radiodensity value of thecoronary plaque and a minimum radiodensity value of the perivascularfat.

Embodiment 41: The method of embodiment 37, wherein generating the ratiocomprises generating a ratio of a minimum radiodensity value of thecoronary plaque and a maximum radiodensity value of the perivascularfat.

Individualized / Subject-Specific CAD Risk Factor Goals

Various embodiments described herein relate to systems, methods, anddevices for determining individualized and/or patient orsubject-specific coronary artery disease (CAD) risk factor goals fromimage-based phenotyping of atherosclerosis. In particular, in someembodiments, the systems, methods, and devices are configured to analyzea medical image of a subject comprising one or more arteries and analyzethe same to perform quantitative phenotyping of atherosclerosis orplaque. For example, quantitative phenotyping can comprise determinationof atherosclerosis burden or volume, type, composition, rate ofprogression or stabilization, and/or the like. In some embodiments, thesystems, methods, and devices described herein can be configured tocorrelate the phenotyping of atherosclerosis to a CAD risk factor levelof the subject to determine an individualized and/or subject orpatient-specific CAD risk factor goal for that particular subject. Forexample, a CAD risk factor goal can be based on LDL or other cholesterollevel, blood pressure, diabetes, tobacco usage, inflammation level,and/or the like. As such, in some embodiments this approach ofpersonalized phenotyping for risk factor goals can allow for developmentof specific treatment targets on a person-by-person basis in a mannerthat can reduce ASCVD events that has not been done to date.

Traditionally, coronary artery disease (CAD) prevention has relied uponthe use of surrogate markers of CAD that have, in population-basedstudies, generally been associated with increased CAD events, such asmyocardial infarction and sudden coronary death. These surrogate markersof CAD can include cholesterol, blood pressure, diabetes mellitus,tobacco use, and family history of premature CAD, amongst others.However, while these approaches can be somewhat effective indiscriminating different populations at risk, they tend to showsignificantly reduced efficacy for pinpointing individuals who willexperience future heart attacks and other atherosclerotic cardiovasculardisease (ASCVD) events. Indeed, certain prior studies have demonstratedthat the coronary lesions that are responsible for heart attacks can bemissed by sole reliance of elevated cholesterol levels in up to 80% ofindividuals who will suffer heart attack. Further, tracking of riskfactors, e.g., cholesterol levels, following administration of medicaltherapy with such agents as statin medications can miss 75% ofindividuals who retain “residual risk” despite effective cholesterollowering and medical treatment. These findings highlight the need formore effective measures of CAD that can be effectively tracked and usedto determine personalized goals of treatment on an individual,patient-by-patient, or subject-by-subject basis.

An additional limitation to traditional CAD risk factors is that it canbe more than the presence or absence of a risk factor that connotes riskof future ASCVD events. Indeed, the presence, extent, severity,duration, treatment, and treatment response can all contribute togetherto whether a specific CAD risk factor may influence the coronaryarteries in a deleterious manner, either alone or in combination withother CAD risk factors. Finally, there are likely an array of unobserved(and heretofore unknown variables) that may contribute to CAD events,including psychosocial, metabolic, inflammatory, environmental, and/orgenetic causes.

Thus, there is an urgent unmet need to identify more precise and/orindividualized measures of CAD risk, particularly one that can integratethe lifetime exposure and treatment effects to the overall manifestationof CAD. To date, there has not been a singular metric that incorporatesall of these factors into a single disease metric that can be used todiagnose, prognosticate risk, guide therapy selection and mostimportantly, provide goals for determining need of additional therapy oradequacy of current therapies.

As such, in some embodiments, the systems, devices, and methodsdescribed herein are configured to address one or more of theshortcomings described above. In particular, in some embodiments, thesystems, devices, and methods described herein are configured toincorporate one or more of such CAD risk factors described above togenerate a metric or measure of patient-specific CAD risk. In someembodiments, the systems, methods, and devices described herein areconfigured to correlate one or more such CAD risk factors with a currentdisease or plaque state of a subject to determine a personalized CADrisk factor goal. For example, rather than setting the same cholesterolor other CAD risk factor goal for everyone, which may not be an accuratemeasure of plaque, atherosclerosis, or disease, some embodimentsdescribed herein are configured to determine a patient orsubject-specific, personalized CAD risk factor goal, such as acholesterol level goal, that more accurately tracks the state of plaque,atherosclerosis, or disease. More specifically, in some embodiments, thesystems, methods, and devices described herein can be configured toanalyze the state of plaque, atherosclerosis, or disease of a subjectand correlate the same to one or more CAD risk factors, such ascholesterol, which can then be used to determine a personalized CAD riskfactor goal for the subject which is specifically derived for thatsubject and has more meaningful correlation to the state of disease forthat individual. Further, based on one or more such analyses, in someembodiments, the systems, devices, and methods described herein can beused to diagnose, prognosticate risk, guide therapy selection, andprovide goals for determining need of additional therapy or adequacy ofcurrent therapies.

As discussed herein, in some embodiments, the systems, devices, andmethods are configured to determine patient-specific coronary arterydisease (CAD) risk factor goals from image-based quantified phenotypingof atherosclerosis of plaque, which can include for examplequantification and characterization of coronary atherosclerosis burden,type, and/or rate of progression. In particular, in some embodiments,systems, methods, and devices described herein allow for determiningindividualized therapeutic goals for CAD risk factor control that aredisease phenotype-based (e.g., burden, type, and/or rate of progressionof disease). In some embodiments, this approach of personalizedphenotyping for risk factor goals allows for development of specifictreatment targets on a person-by-person basis in a manner that canreduce ASCVD events that has not been done to date.

FIG. 27 is a block diagram illustrating an example embodiment(s) ofsystems, devices, and methods for determining patient-specific and/orsubject-specific coronary artery disease (CAD) risk factor goals fromimage-based quantified phenotyping of atherosclerosis.

As illustrated in FIG. 27 , in some embodiments, the system can beconfigured to access and/or determine the level of a CAD risk factor ofan individual, subject, or patient at block 2702. For example, in someembodiments, the CAD risk factor can comprise low-density lipoprotein(LDL) cholesterol, high-density lipoprotein (HDL) cholesterol level,cholesterol particle size and fluffiness, other measures and/or types ofcholesterol, inflammation, glycosylated hemoglobin, blood pressure,and/or the like. In some embodiments, the CAD risk factor can includeany other factor that is used to diagnose and/or correlate with CAD.

In some embodiments, the system can be configured to access a medicalimage of the individual, subject, or patient at block 2704. In someembodiments, the medical image can include one or more arteries, such ascoronary, carotid, aorta, lower extremity, and/or other arteries of thesubject. In some embodiments, the medical image can be stored in amedical image database 2706. In some embodiments, the medical imagedatabase 2706 can be locally accessible by the system and/or can belocated remotely and accessible through a network connection. Themedical image can comprise an image obtained by one or more modalities,such as computed tomography (CT), contrast-enhanced CT, non-contrast CT,x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), MRimaging, optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), and/or near-field infrared spectroscopy (NIRS). Insome embodiments, the medical image comprises one or more of acontrast-enhanced CT image, non-contrast CT image, MR image, and/or animage obtained using any of the modalities described above.

In some embodiments, at block 2708, the system can be configured toperform quantitative phenotyping of atherosclerosis for the individual,subject, or patient. For example, in some embodiments, the quantitativephenotyping can be of atherosclerosis burden, volume, type, composition,and/or rate of progression for the individual or patient. In someembodiments, the system can be configured to utilize one or more imageprocessing, artificial intelligence (AI), and/or machine learning (ML)algorithms to automatically and/or dynamically perform quantitativephenotyping of atherosclerosis. For example, in some embodiments, thesystem can be configured to automatically and/or dynamically identifyone or more arteries, vessels, and/or a portion thereof on the medicalimage, identify one or more regions of plaque, and/or performquantitative phenotyping of plaque.

In some embodiments, as part of quantitative phenotyping, the system canbe configured to identify and/or characterize different types and/orregions of plaque, for example based on density, absolute density,material density, relative density, and/or radiodensity. For example, insome embodiments, the system can be configured to characterize a regionof plaque into one or more sub-types of plaque. For example, in someembodiments, the system can be configured to characterize a region ofplaque as one or more of low density non-calcified plaque, non-calcifiedplaque, or calcified plaque. In some embodiments, calcified plaque cancorrespond to plaque having a highest density range, low densitynon-calcified plaque can correspond to plaque having a lowest densityrange, and non-calcified plaque can correspond to plaque having adensity range between calcified plaque and low density non-calcifiedplaque. For example, in some embodiments, the system can be configuredto characterize a particular region of plaque as low densitynon-calcified plaque when the radiodensity of an image pixel or voxelcorresponding to that region of plaque is between about -189 and about30 Hounsfield units (HU). In some embodiments, the system can beconfigured to characterize a particular region of plaque asnon-calcified plaque when the radiodensity of an image pixel or voxelcorresponding to that region of plaque is between about 31 and about 350HU. In some embodiments, the system can be configured to characterize aparticular region of plaque as calcified plaque when the radiodensity ofan image pixel or voxel corresponding to that region of plaque isbetween about 351 and about 2500 HU.

In some embodiments, the lower and/or upper Hounsfield unit boundarythreshold for determining whether a plaque corresponds to one or more oflow density non-calcified plaque, non-calcified plaque, and/or calcifiedplaque can be about -1000 HU, about -900 HU, about -800 HU, about -700HU, about -600 HU, about -500 HU, about -400 HU, about -300 HU, about-200 HU, about -190 HU, about -180 HU, about -170 HU, about -160 HU,about -150 HU, about -140 HU, about -130 HU, about -120 HU, about -110HU, about -100 HU, about -90HU, about -80 HU, about -70 HU, about -60HU, about -50 HU, about -40 HU, about -30 HU, about -20 HU, about -10HU, about 0 HU, about 10 HU, about 20 HU, about 30 HU, about 40 HU,about 50 HU, about 60 HU, about 70 HU, about 80 HU, about 90 HU, about100 HU, about 110 HU, about 120 HU, about 130 HU, about 140 HU, about150 HU, about 160 HU, about 170 HU, about 180 HU, about 190 HU, about200 HU, about 210 HU, about 220 HU, about 230 HU, about 240 HU, about250 HU, about 260 HU, about 270 HU, about 280 HU, about 290 HU, about300 HU, about 310 HU, about 320 HU, about 330 HU, about 340 HU, about350 HU, about 360 HU, about 370 HU, about 380 HU, about 390 HU, about400 HU, about 410 HU, about 420 HU, about 430 HU, about 440 HU, about450 HU, about 460 HU, about 470 HU, about 480 HU, about 490 HU, about500 HU, about 510 HU, about 520 HU, about 530 HU, about 540 HU, about550 HU, about 560 HU, about 570 HU, about 580 HU, about 590 HU, about600 HU, about 700 HU, about 800 HU, about 900 HU, about 1000 HU, about1100 HU, about 1200 HU, about 1300 HU, about 1400 HU, about 1500 HU,about 1600 HU, about 1700 HU, about 1800 HU, about 1900 HU, about 2000HU, about 2100 HU, about 2200 HU, about 2300 HU, about 2400 HU, about2500 HU, about 2600 HU, about 2700 HU, about 2800 HU, about 2900 HU,about 3000 HU, about 3100 HU, about 3200 HU, about 3300 HU, about 3400HU, about 3500 HU, and/or about 4000 HU.

In some embodiments, the system can be configured to determine and/orcharacterize the burden of atherosclerosis based at least part on volumeof plaque. In some embodiments, the system can be configured to analyzeand/or determine total volume of plaque and/or volume of low-densitynon-calcified plaque, non-calcified plaque, and/or calcified plaque. Insome embodiments, the system can be configured to perform phenotyping ofplaque by determining a ratio of one or more of the foregoing volumes ofplaque, for example within an artery, lesion, vessel, and/or the like.

In some embodiments, the system can be configured to analyze theprogression of plaque. For example, in some embodiments, the system canbe configured to analyze the progression of one or more particularregions of plaque and/or overall progression and/or lesion and/orartery-specific progression of plaque. In some embodiments, in order toanalyze the progression of plaque, the system can be configured toanalyze one or more serial images of the subject for phenotypingatherosclerosis. In some embodiments, tracking the progression of plaquecan comprise analyzing changes and/or lack thereof in total plaquevolume and/or volume of low-density non-calcified plaque, non-calcifiedplaque, and/or calcified plaque. In some embodiments, tracking theprogression of plaque can comprise analyzing changes and/or lack thereofin density of a particular region of plaque and/or globally.

In some embodiments, at block 2710, the system can be configured todetermine a correlation of the baseline risk factor level of the subjectwith the quantitative phenotyping of atherosclerosis. In someembodiments, the system can be configured to utilize one or moremultivariable regression analyses, artificial intelligence (AI), and/ormachine learning (ML) algorithms to automatically and/or dynamicallydetermine a correlation between the risk factor level of the subjectwith results of quantitative phenotyping of atherosclerosis. Forexample, there can be a correlation between one or more quantitativeplaque phenotyping variables and one or more CAD risk level factors.Such correlation can be subject-dependent, meaning that such correlationcan be different and/or the same among different subjects. In someembodiments, the system can utilize an AI and/or ML algorithm trained ona plurality of subject data sets with known one or more quantitativeplaque phenotyping variables and one or more CAD risk level factors todetermine one or more distinct patterns which can be applied to a newsubject.

Generally speaking, even if two people have the exact same quantifiedplaque phenotyping, whether based on volume, composition, rate ofprogression, and/or the like, they can still show different CAD riskfactor levels, such as for example different LDL cholesterol levels. Assuch, subjecting everyone to the same CAD risk factor level goal, suchas for example a particular LDL cholesterol level, may not have the samedesired effect on atherosclerosis which can be thought of as the actualdisease. As such, some systems, devices, and methods described hereinprovide for individualized, subject-specific CAD risk factor goals thatwill actually have a meaningful impact on atherosclerosis and risk ofCAD. In particular, it can be advantageous to maintain the total amountor volume of plaque while hardening existing plaque, for example bychanging more low-density non-calcified plaque and/or non-calcifiedplaque into calcified plaque. By being able to estimate how a change ina particular CAD risk factor level will actually affect a quantifiedplaque measure or variable for a subject, in some embodiments, thesystem can be used to generate and/or facilitate generation of effectivepatient-specific or subject-specific treatment(s).

As discussed herein, in some embodiments, one or more quantifiedatherosclerosis phenotyping and/or measures and/or variables can becorrelated to one or more CAD risk factor levels of a particularsubject. In some embodiments, the system can be configured to access areference values database 2716 to facilitate determination of suchcorrelation. In some embodiments, the reference values database 2716 canbe locally accessible by the system and/or can be located remotely andaccessible through a network connection. In some embodiments, thereference values database 2716 can comprise a plurality of CAD riskfactor levels and/or quantified atherosclerosis phenotyping derived froma plurality of subjects, from which the system can be configured todetermine the correlation between one or more quantified atherosclerosisphenotyping and one or more CAD risk factors for the subject. In someembodiments, the system can be configured to utilize such correlation toestimate the effect of how much a particular change in a particular CADrisk level factor will affect a particular quantified atherosclerosisphenotyping for that subject.

In some embodiments, at block 2712, the system can be configured todetermine a threshold and/or thresholds of one or more quantitativeatherosclerosis phenotyping measures or variables that will cause thesubject to be considered to have elevated and/or normal risk of CAD. Forexample, in some embodiments, one or more threshold values of one ormore quantitative phenotyping measures or variables can be tied tonormal, low, medium, or high risk of CAD. In some embodiments, one ormore threshold values of one or more quantitative phenotyping measuresor variables can be tied to a percentage and/or normal distribution ofrisk of CAD among a wider population, such as for example the average,75^(th) percentile, 90^(th) percentile, and/or the like. In someembodiments, the percentage and/or normal distribution of CAD risk canbe for asymptomatic and/or symptomatic population at large and/or for anage and/or gender group of the subject and/or other group determined byanother clinical factor.

In some embodiments, the system can be configured to determine athreshold that is specific for that particular individual or patient,rather than one that applies to the population at large. In someembodiments, the determined threshold can be applicable to a number or agroup of individuals, for example of those that share one or more commoncharacteristics. For example, for a particular subject, the system candetermine that a particular volume of total plaque, non-calcifiedplaque, low-density non-calcified plaque, calcified plaque, and/or aratio or thereof corresponds to a particular elevated or normal risk ofCAD for the subject. In doing so, in some embodiments, the system can beconfigured to access the reference values database 2716. In someembodiments, the system can be configured to utilize one or more AIand/or ML algorithms to determine one or more subject-specificthresholds of one or more quantitative phenotyping of atherosclerosis tolower the risk of CAD for the subject.

In some embodiments, at block 2714, the system can be configured to setor determine a CAD risk factor level goal for the individual or patient,for example based on the determined one or more thresholds ofquantitative phenotyping of atherosclerosis. As discussed herein, insome embodiments, the determined CAD risk factor goal can beindividualized and/or patient-specific. For example, in someembodiments, the system can be configured to set a patient-specific orsubject-specific LDL cholesterol goal for that individual that isexpected to lower one or more quantified atherosclerosis phenotyping toa desired level. In some embodiments, the system can be configured toaccess the reference values database 2716 in determining asubject-specific CAD risk factor level goal. In some embodiments, thesystem can be configured to utilize one or more AI and/or ML algorithmsto determine a subject-specific CAD risk factor level goal.

In some embodiments, at block 2718, the system can be configureddetermine a proposed treatment for the individual based on the set riskfactor goal, which can be used to treat the patient. For example, insome embodiments, the system can generate a proposed treatment fortreating the patient to an LDL cholesterol level that is associated withnormal or low atherosclerosis burden, type, and/or rate of progressionand/or any other type of quantified phenotyping of atherosclerosis. Insome embodiments, the system can be configured to access a risk /treatment database 2720 in determining a proposed treatment for thesubject. In some embodiments, the risk / treatment database 2720 can belocally accessible by the system and/or can be located remotely andaccessible through a network connection. In some embodiments, the risk /treatment database 2720 can comprise a plurality of treatments that weregiven to patients for lowering risk of CAD, with or without longitudinaltreatment results, and/or one or more quantified atherosclerosisphenotyping variables and/or one or more CAD risk factor level data. Insome embodiments, the system can be configured to utilize one or more AIand/or ML algorithms to determine a subject-specific proposed treatmentfor lowering risk of CAD. In some embodiments, the proposed treatmentcan include one or more of medical intervention, such as a stentimplantation or other procedure, medical treatment, such as prescriptionof statins or some other pharmaceutical, and/or lifestyle change, suchas exercise or dietary changes.

In some embodiments, as atherosclerosis burden, volume, composition,type, and/or rate of progression may be dynamic, the system can beconfigured to perform serial quantified phenotyping of atherosclerosisand re-calibrate and/or update the threshold of a risk factor for thepatient, such as for example LDL. As such, in some embodiments, in someembodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 2702-2720.

As such, in some embodiments, the systems, devices, and methodsdescribed herein can be configured to leverage quantitative diseasephenotyping to determine individual thresholds of risk factor controlvs. lack of control. Further, in some embodiments, armed with thisinformation, treatment targets for risk factors can be custom-made forindividuals rather than relying on population-based estimates thataverage across a group of individuals.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 27B. The example computer system 2730 is incommunication with one or more computing systems 2750 and/or one or moredata sources 2752 via one or more networks 2748. While FIG. 27Billustrates an embodiment of a computing system 2730, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 2730 can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 2730 can comprise Patient-Specific Risk Factor GoalDetermination and/or Tracking Module 2744 that carries out thefunctions, methods, acts, and/or processes described herein. ThePatient-Specific Risk Factor Goal Determination and/or Tracking Module2744 is executed on the computer system 2730 by a central processingunit 2736 discussed further below.

In general the word “module,” as used herein, refers to logic embodiedin hardware or firmware or to a collection of software instructions,having entry and exit points. Modules are written in a program language,such as JAVA, C, or C++, or the like. Software modules can be compiledor linked into an executable program, installed in a dynamic linklibrary, or can be written in an interpreted language such as BASIC,PERL, LAU, PHP or Python and any such languages. Software modules can becalled from other modules or from themselves, and/or can be invoked inresponse to detected events or interruptions. Modules implemented inhardware include connected logic units such as gates and flip-flops,and/or can include programmable units, such as programmable gate arraysor processors.

Generally, the modules described herein refer to logical modules thatcan be combined with other modules or divided into sub-modules despitetheir physical organization or storage. The modules are executed by oneor more computing systems, and can be stored on or within any suitablecomputer readable medium, or implemented in-whole or in-part withinspecial designed hardware or firmware. Not all calculations, analysis,and/or optimization require the use of computer systems, though any ofthe above-described methods, calculations, processes, or analyses can befacilitated through the use of computers. Further, in some embodiments,process blocks described herein can be altered, rearranged, combined,and/or omitted.

The computer system 2730 includes one or more processing units (CPU)2736, which can comprise a microprocessor. The computer system 2730further includes a physical memory 2740, such as random access memory(RAM) for temporary storage of information, a read only memory (ROM) forpermanent storage of information, and a mass storage device 2734, suchas a backing store, hard drive, rotating magnetic disks, solid statedisks (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory,diskette, or optical media storage device. Alternatively, the massstorage device can be implemented in an array of servers. Typically, thecomponents of the computer system 2730 are connected to the computerusing a standards based bus system. The bus system can be implementedusing various protocols, such as Peripheral Component Interconnect(PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) andExtended ISA (EISA) architectures.

The computer system 2730 includes one or more input/output (I/O) devicesand interfaces 2742, such as a keyboard, mouse, touch pad, and printer.The I/O devices and interfaces 2742 can include one or more displaydevices, such as a monitor, that allows the visual presentation of datato a user. More particularly, a display device provides for thepresentation of GUIs as application software data, and multi-mediapresentations, for example. The I/O devices and interfaces 2742 can alsoprovide a communications interface to various external devices. Thecomputer system 2730 can comprise one or more multi-media devices 208,such as speakers, video cards, graphics accelerators, and microphones,for example.

Computing System Device / Operating System

The computer system 2730 can run on a variety of computing devices, suchas a server, a Windows server, a Structure Query Language server, a UnixServer, a personal computer, a laptop computer, and so forth. In otherembodiments, the computer system 2730 can run on a cluster computersystem, a mainframe computer system and/or other computing systemsuitable for controlling and/or communicating with large databases,performing high volume transaction processing, and generating reportsfrom large databases. The computing system 2730 is generally controlledand coordinated by an operating system software, such as z/OS, Windows,Linux, UNIX, BSD, PHP, SunOS, Solaris, MacOS, ICloud services or othercompatible operating systems, including proprietary operating systems.Operating systems control and schedule computer processes for execution,perform memory management, provide file system, networking, and I/Oservices, and provide a user interface, such as a graphical userinterface (GUI), among other things.

Network

The computer system 2730 illustrated in FIG. 27B is coupled to a network2748, such as a LAN, WAN, or the Internet via a communication link 2746(wired, wireless, or a combination thereof). Network 2748 communicateswith various computing devices and/or other electronic devices. Network2748 is communicating with the one or more computing systems 2750 andthe one or more data sources 2752. The Patient-Specific Risk Factor GoalDetermination and/or Tracking Module 2744 can access or can be accessedby computing systems 2750 and/or data sources 2752 through a web-enableduser access point. Connections can be a direct physical connection, avirtual connection, and other connection type. The web-enabled useraccess point can comprise a browser module that uses text, graphics,audio, video, and other media to present data and to allow interactionwith data via the network 218.

The output module can be implemented as a combination of an all-pointsaddressable display such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. The output module can be implemented to communicate with inputdevices 2742 and they also include software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements, such as menus, windows, dialogue boxes, tool bars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the output module can communicate with a set ofinput and output devices to receive signals from the user.

Other Systems

The computing system 2730 can include one or more internal and/orexternal data sources (for example, data sources 2752). In someembodiments, one or more of the data repositories and the data sourcesdescribed above can be implemented using a relational database, such asDB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well asother types of databases such as a flat-file database, an entityrelationship database, and object-oriented database, and/or arecord-based database.

The computer system 2730 can also access one or more data sources (ordatabases) 2752. The databases 2752 can be stored in a database or datarepository. The computer system 2730 can access the one or moredatabases 2752 through a network 2748 or can directly access thedatabase or data repository through I/O devices and interfaces 2742. Thedata repository storing the one or more databases 2752 can reside withinthe computer system 2730.

URLs and Cookies

In some embodiments, one or more features of the systems, methods, anddevices described herein can utilize a URL and/or cookies, for examplefor storing and/or transmitting data or user information. A UniformResource Locator (URL) can include a web address and/or a reference to aweb resource that is stored on a database and/or a server. The URL canspecify the location of the resource on a computer and/or a computernetwork. The URL can include a mechanism to retrieve the networkresource. The source of the network resource can receive a URL, identifythe location of the web resource, and transmit the web resource back tothe requestor. A URL can be converted to an IP address, and a Doman NameSystem (DNS) can look up the URL and its corresponding IP address. URLscan be references to web pages, file transfers, emails, databaseaccesses, and other applications. The URLs can include a sequence ofcharacters that identify a path, domain name, a file extension, a hostname, a query, a fragment, scheme, a protocol identifier, a port number,a username, a password, a flag, an object, a resource name and/or thelike. The systems disclosed herein can generate, receive, transmit,apply, parse, serialize, render, and/or perform an action on a URL.

Examples of Embodiments Relating to Determining Patient Specific RiskFactor Goals From Image-Based Quantification

The following are non-limiting examples of certain embodiments ofsystems and methods for determining patient specific risk factor goalsand/or other related features. Other embodiments may include one or moreother features, or different features, that are discussed herein.

Embodiment 1: A computer-implemented method for determiningpatient-specific coronary artery disease (CAD) risk factor goals basedon quantification of coronary atherosclerosis and vascular morphologyfeatures using non-invasive medical image analysis, the methodcomprising: accessing, by a computer system, a CAD risk factor level fora subject; accessing, by the computer system, a medical image of thesubject, the medical image comprising one or more coronary arteries;analyzing, by the computer system, the medical image of the subject toperform quantitative phenotyping of atherosclerosis and vascularmorphology, the quantitative phenotyping of atherosclerosis comprisinganalysis of one or more of plaque volume, plaque composition, or plaqueprogression; determining, by the computer system, correlation of the CADrisk factor level with the quantitative phenotyping of atherosclerosisand vascular morphology; determining, by the computer system, anindividualized CAD risk factor level threshold of elevated risk of CADfor the subject based at least in part on the CAD risk factor level andthe determined correlation of the CAD risk factor level with thequantitative phenotyping of atherosclerosis and vascular morphology; anddetermining, by the computer system, a subject-specific goal for the CADrisk factor level based at least in part on the determinedindividualized CAD risk factor level threshold of elevated risk of CADfor the subject, wherein the determined subject-specific goal for theCAD risk factor level is configured to be used to determine anindividualized treatment for the subject, wherein the computer systemcomprises a computer processor and an electronic storage medium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereinthe CAD risk factor level comprises one or more of cholesterol level,low-density lipoprotein (LDL) cholesterol level, high-densitylipoprotein (HDL) cholesterol level, cholesterol particle size andfluffiness, inflammation level, glycosylated hemoglobin, or bloodpressure.

Embodiment 3: The computer-implemented method of Embodiments 1 or 2,wherein the quantitative phenotyping of atherosclerosis is performedbased at least in part on analysis of density values of one or morepixels of the medical image corresponding to plaque.

Embodiment 4: The computer-implemented method of any one of Embodiments1-3, wherein the plaque volume comprises one or more of total plaquevolume, calcified plaque volume, non-calcified plaque volume, orlow-density non-calcified plaque volume.

Embodiment 5: The computer-implemented method of Embodiment 3, whereinthe density values comprise radiodensity values.

Embodiment 6: The computer-implemented method of any one of Embodiments3-5, wherein the plaque composition comprises composition of one or moreof calcified plaque, non-calcified plaque, or low-density non-calcifiedplaque.

Embodiment 7: The computer-implemented method of Embodiment 6, whereinone or more of the calcified plaque, non-calcified plaque, oflow-density non-calcified plaque is identified based at least in part onradiodensity values of one or more pixels of the medical imagecorresponding to plaque.

Embodiment 8: The computer-implemented method of Embodiment 7, whereincalcified plaque comprises one or more pixels of the medical image withradiodensity values of between about 351 and about 2500 Hounsfieldunits, non-calcified plaque comprises one or more pixels of the medicalimage with radiodensity values of between about 31 and about 250Hounsfield units, and low-density non-calcified plaque comprises one ormore pixels of the medical image with radiodensity values of betweenabout -189 and about 30 Hounsfield units.

Embodiment 9: The computer-implemented method of any of Embodiments 1 to8, wherein the plaque progression is determined by: accessing, by thecomputer system, one or more serial medical images of the patient, theone or more serial medical images comprising one or more coronaryarteries; and analyzing, by the computer system, the one or more serialmedical images of the patient to determine plaque progression based atleast in part on a serial change in plaque volume.

Embodiment 10: The computer-implemented method of Embodiment 9, whereinthe serial change in plaque volume is based on one or more of totalplaque volume, calcified plaque volume, non-calcified plaque volume, orlow-density non-calcified plaque volume.

Embodiment 11: The computer-implemented method of any one of Embodiments1-10, wherein the vascular morphology comprises one or more of absoluteminimum lumen diameter or area, lumen diameter, cross-sectional lumenarea, vessel volume, lumen volume, arterial remodeling, vessel or lumengeometry, or vessel or lumen curvature.

Embodiment 12: The computer-implemented method of any one of Embodiments1-11, wherein the correlation of the CAD risk factor level with thequantitative phenotyping of atherosclerosis is determined based at leastin part by multivariable regression analysis.

Embodiment 13: The computer-implemented method of any one of Embodiments1-12, wherein the correlation of the CAD risk factor level with thequantitative phenotyping of atherosclerosis is determined based at leastin part by a machine learning algorithm.

Embodiment 14: The computer-implemented method of any one of Embodiments1-13, wherein the medical image comprises a Computed Tomography (CT)image.

Embodiment 15: The computer-implemented method of any on of Embodiments1-14, wherein the medical image is obtained using an imaging techniquecomprising one or more of CT, x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), MR imaging, optical coherencetomography (OCT), nuclear medicine imaging, positron-emission tomography(PET), single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).

Embodiment 16: The computer-implemented method of any one of Embodiments1-15, wherein the treatment for cardiovascular disease comprises medicalintervention, medical treatment, or lifestyle interventions, includingbut not limited to changes in diet, physical activity, anxiety andstress level, sleep and others.

Embodiment 17: The computer-implemented method of any one of Embodiments1-16, further comprising: accessing, by the computer system, a secondmedical image of the subject, the second medical image obtained at alater point in time than the medical image; analyzing, by the computersystem, the second medical image of the subject to perform quantitativephenotyping of atherosclerosis; recalibrating, by the computer system,the individualized CAD risk factor level threshold of elevated risk ofCAD for the subject based at least in part on the quantitativephenotyping of atherosclerosis of the second medical image; andupdating, by the computer system, the subject-specific goal for the CADrisk factor level based at least in part on the recalibratedindividualized CAD risk factor level threshold of elevated risk of CADfor the subject, wherein the updated subject-specific goal for the CADrisk factor level is configured to used to change or maintain theindividualized treatment for the subject.

Embodiment 18: A system for determining patient-specific coronary arterydisease (CAD) risk factor goals based on quantification of coronaryatherosclerosis using non-invasive medical image analysis, the systemcomprising: one or more computer readable storage devices configured tostore a plurality of computer executable instructions; and one or morehardware computer processors in communication with the one or morecomputer readable storage devices and configured to execute theplurality of computer executable instructions in order to cause thesystem to: access a CAD risk factor level for a subject; access amedical image of the subject, the medical image comprising one or morecoronary arteries; analyze the medical image of the subject to performquantitative phenotyping of atherosclerosis, the quantitativephenotyping of atherosclerosis comprising analysis of one or more ofplaque volume, plaque composition, or plaque progression; determinecorrelation of the CAD risk factor level with the quantitativephenotyping of atherosclerosis; determine an individualized CAD riskfactor level threshold of elevated risk of CAD for the subject based atleast in part on the CAD risk factor level and the determinedcorrelation of the CAD risk factor level with the quantitativephenotyping of atherosclerosis; and determine a subject-specific goalfor the CAD risk factor level based at least in part on the determinedindividualized CAD risk factor level threshold of elevated risk of CADfor the subject, wherein the determined subject-specific goal for theCAD risk factor level is configured to be used to determine anindividualized treatment for the subject.

Embodiment 19: The system of Embodiment 18, wherein the CAD risk factorlevel comprises one or more of cholesterol level, low-densitylipoprotein (LDL) cholesterol level, high-density lipoprotein (HDL)cholesterol level, cholesterol particle size and fluffiness,inflammation level, glycosylated hemoglobin, or blood pressure.

Embodiment 20: The system of Embodiments 18 or 19, wherein thequantitative phenotyping of atherosclerosis is performed based at leastin part on analysis of density values of one or more pixels of themedical image corresponding to plaque.

Embodiment 21: The system of any one of Embodiments 18-21, wherein thedensity values comprises radiodensity values.

Embodiment 22: The system of any one of Embodiments 18-22, wherein theplaque volume comprises one or more of total plaque volume, calcifiedplaque volume, non-calcified plaque volume, or low-density non-calcifiedplaque volume.

Embodiment 23: The system of Embodiment 21, wherein the plaquecomposition comprises composition of one or more of calcified plaque,non-calcified plaque, or low-density non-calcified plaque.

Embodiment 24: The system of any one of Embodiments 18-23, wherein theplaque progression is determined by: accessing, by the computer system,one or more serial medical images of the patient, the one or more serialmedical images comprising one or more coronary arteries; and analyzing,by the computer system, the one or more serial medical images of thepatient to determine plaque progression based at least in part on aserial change in plaque volume.

Embodiment 25: The system of any one of Embodiments 22-24, wherein theserial change in plaque volume is based on one or more of total plaquevolume, calcified plaque volume, non-calcified plaque volume, orlow-density non-calcified plaque volume.

Embodiment 26: The system of any one of Embodiments 18-25, wherein thecorrelation of the CAD risk factor level with the quantitativephenotyping of atherosclerosis is determined based at least in part bymultivariable regression analysis.

Embodiment 27: The system of any one of Embodiments 18-26, wherein thecorrelation of the CAD risk factor level with the quantitativephenotyping of atherosclerosis is determined based at least in part by amachine learning algorithm.

Embodiment 28: The system of any one of Embodiments 18-27, wherein themedical image comprises a Computed Tomography (CT) image.

Embodiment 29: The system of any one of Embodiments 18-28, wherein themedical image is obtained using an imaging technique comprising one ormore of CT, x-ray, ultrasound, echocardiography, intravascularultrasound (IVUS), MR imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS).

Embodiment 30: The system of any one of Embodiments 18-30, wherein thetreatment for cardiovascular disease comprises medical intervention,medical treatment, or lifestyle change.

Embodiment 31: The system of any one of Embodiments 18-30, wherein thesystem is further caused to: access a second medical image of thesubject, the second medical image obtained at a later point in time thanthe medical image; analyze the second medical image of the subject toperform quantitative phenotyping of atherosclerosis; recalibrate theindividualized CAD risk factor level threshold of elevated risk of CADfor the subject based at least in part on the quantitative phenotypingof atherosclerosis of the second medical image; and update thesubject-specific goal for the CAD risk factor level based at least inpart on the recalibrated individualized CAD risk factor level thresholdof elevated risk of CAD for the subject, wherein the updatedsubject-specific goal for the CAD risk factor level is configured toused to change or maintain the individualized treatment for the subject.

Embodiment 32: The system of any one of Embodiments 18-25, wherein thesystem is further caused to analyze the medical image of the subject toperform phenotyping of vascular morphology, wherein the subject-specificgoal for the CAD risk factor level is further determined based at leastin part on the phenotyping of vascular morphology, the vascularmorphology comprising one or more of absolute minimum lumen diameter orarea, lumen diameter, cross-sectional lumen area, vessel volume, lumenvolume, arterial remodeling, vessel or lumen geometry, or wherein thesubject-specific goal for the CAD risk factor level is furtherdetermined based at least in part on the phenotyping of vascularmorphology, the vascular morphology comprising one or more of absoluteminimum lumen diameter or area, lumen diameter, cross-sectional lumenarea, vessel volume, lumen volume, arterial remodeling, vessel or lumengeometry, or vessel or lumen curvature.

Automated Diagnosis, Risk Assessment, and Characterization of HeartDisease

Generally speaking, heart disease or a major adverse cardiovascularevent (MACE) or arterial disease, such as coronary artery disease (CAD)or periphery artery disease (PAD) can be extremely difficult to diagnoseuntil a patient becomes very symptomatic. This can be due to the factthat existing methods focus on detecting severe and/or physical symptomswhich typically arise only in later stages of heart disease, such as forexample active chest pain, active heart attack, cardiogenic shock,and/or the like. In addition, risk of heart disease, MACE can bedependent on a number of different factors and/or variables, making itdifficult to diagnose, characterize, and/or predict. As used herein,MACE can refer to one or more of a stroke, myocardial infarction,cardiovascular death, admission for heart failure, ischemiccardiovascular events, cardiac death, hospitalization for heart failure,angina pain, cardiovascular-related illness, cardiac arrest, heartattack, and/or the like.

In some embodiments, the systems, devices, and methods described hereinaddress such technical shortcomings by providing an image-based and/ornon-invasive approach to diagnose, characterize, predict, and/orotherwise assess risk of MACE or arterial disease of a subject by takinginto account one or more analyses, for example of coronaryatherosclerosis, aortic atherosclerosis, and/or emphysema. Coronaryatherosclerosis, aortic atherosclerosis, and/or emphysema can all beconsidered a cause, factor, and/or variable in risk of MACE or arterialdisease. However, existing technologies fail to provide a comprehensivesolution that can take such multiple factors into consideration inassessing risk of MACE or arterial disease. In addition, theinterrelation between coronary atherosclerosis, aortic atherosclerosis,and/or emphysema when assessing risk of MACE or arterial disease can bedifficult to ascertain. As such, in some embodiments, the systems,methods, and devices are configured to determine a likelihood and/orrisk of MACE or arterial disease based on inputs of one or more ofcoronary atherosclerosis, aortic atherosclerosis, and/or emphysema, forexample utilizing a machine learning (ML) and/or artificial intelligence(AI) algorithm(s). In some embodiments, by the combination of analyzingcoronary atherosclerosis, aortic atherosclerosis, and emphysema canprovide synergistic effects in more accurately determining the risk ofMACE and/or arterial disease. Moreover, it can be advantageous tonon-invasively determine risk of MACE or arterial disease instead ofusing invasive measures, such as for example a stress test and/or thelike. As such, in some embodiments, the systems, methods, and devicescan be configured to analyze one or more images obtained non-invasivelyto derive, phenotype, characterize, quantify, and/or otherwise analyzecoronary atherosclerosis, aortic atherosclerosis, and/or emphysema, theresults of which can then be used to diagnose, assess, and/orcharacterize risk of MACE or arterial disease for a subject, therebyproviding a multi-factor and/or non-invasive approach to MACE orarterial disease risk assessment. Such risk assessment can further beused to generate a proposed treatment for a subject for lowering and/ormaintaining risk of MACE or arterial disease.

In some embodiments, the system can be configured to analyze justcoronary and aortic atherosclerosis, and not emphysema, in assessingrisk of MACE or arterial disease. In some embodiments, the system can beconfigured to analyze just coronary atherosclerosis and emphysema inassessing risk of MACE or arterial disease. In some embodiments, thesystem can be configured to analyze coronary atherosclerosis, aorticatherosclerosis, and emphysema in assessing risk of MACE or arterialdisease.

In some embodiments, the system can be configured to utilize a referencedatabase with risk assessments of MACE or arterial disease based on oneor more of coronary atherosclerosis, aortic atherosclerosis, and/oremphysema to generate a population-based percentage of risk of MACE orarterial disease for a subject. In some embodiments, thepopulation-based percentage can be based on one or more other factors,such as for example age, gender, ethnicity, and/or risk factors.

In particular, in some embodiments, the systems, devices, and methodsdescribed herein are configured to diagnose, characterize, assess therisk of, and/or augment or enhance the diagnosis of MACE, heart disease,coronary heart disease, coronary atherosclerotic disease, arterialdisease and/or the like on a sub-clinical level. Further, in someembodiments, the systems, devices, and methods described herein areconfigured to diagnose, characterize, assess the risk of, and/or augmentor enhance the diagnosis of MACE, arterial disease, heart disease,coronary heart disease, coronary atherosclerotic disease, and/or thelike utilizing one or more image analysis techniques and/or processes.In some embodiments, the systems, devices, and methods described hereinare configured to diagnose, characterize, assess the risk of, and/oraugment or enhance the diagnosis of MACE, arterial disease, heartdisease, coronary heart disease, coronary atherosclerotic disease,and/or the like even when the subject has not experienced any physicalsymptoms, such as active chest pain, active heart attack, cardiogenicshock, and/or the like. In some embodiments, the systems, devices, andmethods described herein are configured to diagnose, characterize,assess the risk of, and/or augment or enhance the diagnosis of MACE,arterial disease, heart disease, coronary heart disease, coronaryatherosclerotic disease, and/or the like without the need to analyze anysuch physical symptoms. In some embodiments, the systems, devices, andmethods described herein are configured to diagnose, characterize,assess the risk of, and/or augment or enhance the diagnosis of MACE,arterial disease, heart disease, coronary heart disease, coronaryatherosclerotic disease, and/or the like utilizing one or more imageanalysis techniques and/or processes and optionally supplementing thesame based on a history of physical symptoms experienced by the subject,such as for example active chest pain, active heart attack, cardiogenicshock, and/or the like. As such, in some embodiments, the systems,methods, and devices described herein can be configured to diagnose,characterize, assess the risk of, and/or augment or enhance thediagnosis of asymptomatic atherosclerosis, such as asymptomatic aorticatherosclerosis and/or asymptomatic coronary atherosclerosis, and/oremphysema.

As discussed herein, in some embodiments, the systems, devices, andmethods described herein can be configured to utilize one or more imageanalysis and/or processing techniques to diagnose, characterize, assessthe risk of, and/or augment or enhance the diagnosis of MACE, arterialdisease, and/or heart disease, whether symptomatic or asymptomatic, suchas for example based on aortic atherosclerosis, coronaryatherosclerosis, emphysema and/or the like. For example, in someembodiments, the systems, methods, and devices can be configured toanalyze one or more medical images of a subject, such as a coronary CTangiography (CCTA), using one or more image processing, artificialintelligence, and/or machine learning techniques. In some embodiments,the systems, methods, and devices described herein can be configured todiagnose, characterize, assess the risk of, and/or augment or enhancethe diagnosis of heart disease by analyzing one or more medical images,such as for example a contrast-enhanced CCTA, non-contrast CT,non-contrast coronary calcium scoring, non-gated contrast or contrastchest CT scans, abdominal CT scan, MRI angiography, x-ray fluoroscopy,and/or the like.

In some embodiments, the systems, methods, and devices described hereincan be configured to utilize analyses of coronary atherosclerosis,aortic atherosclerosis, and/or emphysema to identify high-risk subjectsof MACE or arterial disease. For example, in some embodiments, thesystem can be configured to utilize analyses of coronaryatherosclerosis, aortic atherosclerosis, and/or emphysema to identifynew formers of plaque or non-calcified plaque, rapid progressors ofplaque or non-calcified plaque, and/or non-responders to medicine ortreatment. More specifically, in some embodiments, the system can beconfigured to utilize one or more plaque parameters, quantified plaquephenotyping, and/or the like described herein, as applied to a coronaryand/or aortic artery, and/or image-based analysis of emphysema for suchanalyses. In some embodiments, the system can be configured to analyzejust coronary and aortic atherosclerosis, and not emphysema, to identifynew formers of plaque or non-calcified plaque, rapid progressors ofplaque or non-calcified plaque, and/or non-responders to medicine ortreatment. In some embodiments, the system can be configured to analyzejust coronary atherosclerosis and emphysema, and not aorticatherosclerosis, to identify new formers of plaque or non-calcifiedplaque, rapid progressors of plaque or non-calcified plaque, and/ornon-responders to medicine or treatment. In some embodiments, the systemcan be configured to analyze coronary atherosclerosis, aorticatherosclerosis, and emphysema to identify new formers of plaque ornon-calcified plaque, rapid progressors of plaque or non-calcifiedplaque, and/or non-responders to medicine or treatment.

In some embodiments, the systems, methods, and devices described hereincan be configured to utilize analyses of coronary atherosclerosis,aortic atherosclerosis, and/or emphysema to determine the likelihood ofperipheral artery disease (PAD). PAD has a worldwide prevalence of morethan 200 million, with an estimated 8-12 million Americans affected. Theprevalence of PAD is expected to increase as the population ages,smoking status persists, and the prevalence of diabetes, hypertension,and obesity grow. Although awareness has improved, PAD is stillassociated with significant morbidity, mortality, and quality of lifeimpairment. Given the substantial prevalence of PAD, it can beimperative that a screening program be undertaken to identify those withhigh risk of PAD, which can be done utilizing one or more systems,devices, and methods described herein.

As a non-limiting example, FIGS. 28A-28B illustrate an exampleembodiment of identification of coronary and aortic disease /atherosclerosis identified on a coronary CT angiogram (CCTA) utilizingembodiments of the systems, devices, and methods described herein. Asillustrated in FIG. 28A, one or more coronary arteries can be imaged aspart of a CCTA and, as illustrated in FIG. 28B, the aorta can also beimaged as part of a CCTA. As such, analysis of coronary and aorticatherosclerosis can be performed together based on a single CCTA in someembodiments that are configured to analyze CCTAs using one or more imageanalysis techniques as described herein, including for example analysisof one or more plaque, fat, and/or vessel parameters. For example, theone or more plaque parameters can include plaque volume, composition,attenuation, location, geometry, and/or any other plaque parametersdescribed herein.

In some embodiments, the systems, devices, and methods described hereincan be configured to utilize the diagnosis, characterization, and/orrisk assessment of heart disease of a subject, such as for examplecoronary and/or aortic atherosclerosis, to further generate a report,treatment, and/or prognosis and/or identify or track resourceutilization for the subject. By utilizing such techniques, in someembodiments, the systems, devices, and methods described herein canallow for early diagnosis and/or treatment of heart disease prior to thesubject experiencing physical symptoms. For example, in someembodiments, the systems, devices, and methods described herein can beconfigured to automatically and/or dynamically place a subject in aparticular vascular or heart disease category based at least in part onthe diagnosis, characterization, and/or risk assessment of heart diseaseof the subject based on image analysis. In some embodiments, thesystems, devices, and methods described herein can be configured tofurther assign a risk-adjusted weight for the subject to anticipateprognosis and/or resource utilization for the subject, for example basedat least in part on the diagnosis, characterization, and/or riskassessment of heart disease of the subject based on image analysisand/or the particular vascular or heart disease category determined forthe subject.

FIG. 28C is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for image-based diagnosis, riskassessment, and/or characterization of a major adverse cardiovascularevent. As illustrated in FIG. 28C, in some embodiments, the system canbe configured to analyze one or more of coronary atherosclerosis, aorticatherosclerosis, and/or emphysema, for example from a medical image, todetermine risk of MACE or arterial disease (AD), such as PAD, for asubject.

In some embodiments, at block 2802, the system can be configured toaccess and/or modify one or more medical images. In some embodiments,the medical image can include one or more arteries, such as coronary,aorta, carotid, and/or other arteries and/or one or more portions of thelungs of a subject. In some embodiments, the medical image can comprisea CCTA. In some embodiments, the medical image can comprise an imagefield that is typically acquired during a CCTA. In some embodiments, themedical image can comprise a larger image field than that is typicallyacquired during a CCTA, for example to capture one or more portions ofthe aorta and/or lungs. In some embodiments, the system can beconfigured to access multiple images, one or more of which captures oneor more portions of the coronary arteries, aorta, and/or lungs. Forexample, in some embodiments, the system can be configured to access onemedical image that comprises one or more portions of the coronaryarteries and/or aorta of the subject and a separate image that comprisesone or more portions of the lungs. In some embodiments, the system canbe configured to access one medical image that comprises one or moreportions of the coronary arteries, one medical image that comprises oneor more portions of the aorta, and one medical image that comprises oneor more portions of the lungs. In some embodiments, the system can beconfigured to access a single medical image that comprises one or moreportions of the coronary arteries, aorta, and the lungs. For example, insome embodiments, the system can be configured to access a single imageacquired from a single image acquisition to analyze one or more portionsof the coronary arteries, aorta, and the lungs to determine risk of MACEor arterial disease, such as PAD, for a subject.

In some embodiments, the medical image can be stored in a medical imagedatabase 2804. In some embodiments, the medical image database 2804 canbe locally accessible by the system and/or can be located remotely andaccessible through a network connection. The medical image can comprisean image obtain using one or more modalities such as for example, CT,Dual-Energy Computed Tomography (DECT), Spectral CT, photon-counting CT,x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS),Magnetic Resonance (MR) imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS). In some embodiments, the medical image comprisesone or more of a contrast-enhanced CT image, non-contrast CT image, MRimage, and/or an image obtained using any of the modalities describedabove.

In some embodiments, the system can be configured to automaticallyand/or dynamically perform one or more analyses of the medical image asdiscussed herein. For example, in some embodiments, at block 2806, thesystem can be configured to identify, analyze, and/or quantify coronaryatherosclerosis. In some embodiments, the system can be configured toperform quantified phenotyping of coronary atherosclerosis. For example,in some embodiments, the quantitative phenotyping can be ofatherosclerosis burden, volume, type, composition, and/or rate ofprogression for the individual or patient. In some embodiments, thesystem can be configured to utilize one or more image processing,artificial intelligence (AI), and/or machine learning (ML) algorithms toautomatically and/or dynamically perform quantitative phenotyping ofatherosclerosis. For example, in some embodiments, the system can beconfigured to automatically and/or dynamically identify one or morearteries, vessels, and/or a portion thereof on the medical image,identify one or more regions of plaque, and/or perform quantitativephenotyping of plaque.

In some embodiments, the system can be configured to identify and/orcharacterize different types and/or regions of coronary atherosclerosisor plaque, for example based on density, absolute density, materialdensity, relative density, and/or radiodensity. In some embodiments, thesystem can be configured to characterize a region of plaque into one ormore sub-types of plaque. For example, in some embodiments, the systemcan be configured to characterize a region of plaque as one or more oflow density non-calcified plaque, non-calcified plaque, or calcifiedplaque. In some embodiments, calcified plaque can correspond to plaquehaving a highest density range, low density non-calcified plaque cancorrespond to plaque having a lowest density range, and non-calcifiedplaque can correspond to plaque having a density range between calcifiedplaque and low density non-calcified plaque. For example, in someembodiments, the system can be configured to characterize a particularregion of plaque as low density non-calcified plaque when theradiodensity of an image pixel or voxel corresponding to that region ofplaque is between about -189 and about 30 Hounsfield units (HU). In someembodiments, the system can be configured to characterize a particularregion of plaque as non-calcified plaque when the radiodensity of animage pixel or voxel corresponding to that region of plaque is betweenabout 31 and about 350 HU. In some embodiments, the system can beconfigured to characterize a particular region of plaque as calcifiedplaque when the radiodensity of an image pixel or voxel corresponding tothat region of plaque is between about 351 and about 2500 HU.

In some embodiments, the lower and/or upper Hounsfield unit boundarythreshold for determining whether a plaque corresponds to one or more oflow density non-calcified plaque, non-calcified plaque, and/or calcifiedplaque can be about -1000 HU, about -900 HU, about -800 HU, about -700HU, about -600 HU, about -500 HU, about -400 HU, about -300 HU, about-200 HU, about -190 HU, about -180 HU, about -170 HU, about -160 HU,about -150 HU, about -140 HU, about -130 HU, about -120 HU, about -110HU, about -100 HU, about -90HU, about -80 HU, about -70 HU, about -60HU, about -50 HU, about -40 HU, about -30 HU, about -20 HU, about -10HU, about 0 HU, about 10 HU, about 20 HU, about 30 HU, about 40 HU,about 50 HU, about 60 HU, about 70 HU, about 80 HU, about 90 HU, about100 HU, about 110 HU, about 120 HU, about 130 HU, about 140 HU, about150 HU, about 160 HU, about 170 HU, about 180 HU, about 190 HU, about200 HU, about 210 HU, about 220 HU, about 230 HU, about 240 HU, about250 HU, about 260 HU, about 270 HU, about 280 HU, about 290 HU, about300 HU, about 310 HU, about 320 HU, about 330 HU, about 340 HU, about350 HU, about 360 HU, about 370 HU, about 380 HU, about 390 HU, about400 HU, about 410 HU, about 420 HU, about 430 HU, about 440 HU, about450 HU, about 460 HU, about 470 HU, about 480 HU, about 490 HU, about500 HU, about 510 HU, about 520 HU, about 530 HU, about 540 HU, about550 HU, about 560 HU, about 570 HU, about 580 HU, about 590 HU, about600 HU, about 700 HU, about 800 HU, about 900 HU, about 1000 HU, about1100 HU, about 1200 HU, about 1300 HU, about 1400 HU, about 1500 HU,about 1600 HU, about 1700 HU, about 1800 HU, about 1900 HU, about 2000HU, about 2100 HU, about 2200 HU, about 2300 HU, about 2400 HU, about2500 HU, about 2600 HU, about 2700 HU, about 2800 HU, about 2900 HU,about 3000 HU, about 3100 HU, about 3200 HU, about 3300 HU, about 3400HU, about 3500 HU, and/or about 4000 HU.

In some embodiments, the system can be configured to determine and/orcharacterize the burden of coronary atherosclerosis based at least parton volume of plaque. In some embodiments, the system can be configuredto analyze and/or determine total volume of coronary plaque and/orvolume of low-density non-calcified plaque, non-calcified plaque, and/orcalcified plaque in the analyzed coronaries. In some embodiments, thesystem can be configured to perform phenotyping of coronaryatherosclerosis by determining a ratio of one or more of the foregoingvolumes of plaque, for example within an artery, lesion, vessel, and/orthe like.

In some embodiments, the system can be configured to analyze theprogression of coronary atherosclerosis. For example, in someembodiments, the system can be configured to analyze the progression ofone or more particular regions of plaque and/or overall progressionand/or lesion and/or artery-specific progression of plaque. In someembodiments, in order to analyze the progression of plaque, the systemcan be configured to analyze one or more serial images of the subjectfor phenotyping atherosclerosis. In some embodiments, tracking theprogression of plaque can comprise analyzing changes and/or lack thereofin total plaque volume and/or volume of low-density non-calcifiedplaque, non-calcified plaque, and/or calcified plaque. In someembodiments, tracking the progression of plaque can comprise analyzingchanges and/or lack thereof in density of a particular region of plaqueand/or globally.

In some embodiments, at block 2812, the system can be configured todetermine a risk of MACE and/or arterial disease, such as PAD, based atleast in part on the results of coronary atherosclerosis analysis and/orquantified phenotyping. In determining risk of MACE and/or arterialdisease, in some embodiments, the system can be configured to access oneor more reference values of quantified phenotyping and/or other analysesof coronary atherosclerosis as compared to risk of MACE or arterialdisease, which can be stored on a coronary atherosclerosis risk database2814. In some embodiments, the one or more reference values ofquantified phenotyping and/or other analyses of coronary atherosclerosisas compared to risk of MACE or arterial disease can be derived from apopulation with varying states of coronary atherosclerosis as comparedto risk of MACE and/or arterial disease. In some embodiments, thecoronary risk database 2814 can be locally accessible by the systemand/or can be located remotely and accessible through a networkconnection. In some embodiments, the system can be configured to utilizeone or more artificial intelligence (AI) and/or machine learning (ML)algorithms to automatically and/or dynamically determine risk of MACE orarterial disease based on coronary plaque analysis.

In some embodiments, at block 2808, the system can be configured toidentify, analyze, and/or quantify aortic atherosclerosis. In someembodiments, the system can be configured to perform quantifiedphenotyping of aortic atherosclerosis. For example, in some embodiments,the quantitative phenotyping can be of atherosclerosis burden, volume,type, composition, and/or rate of progression for the individual orpatient. In some embodiments, the system can be configured to utilizeone or more image processing, artificial intelligence (AI), and/ormachine learning (ML) algorithms to automatically and/or dynamicallyperform quantitative phenotyping of aortic atherosclerosis. For example,in some embodiments, the system can be configured to automaticallyand/or dynamically identify one or more arteries, vessels, and/or aportion thereof on the medical image, identify one or more regions ofplaque, and/or perform quantitative phenotyping of plaque.

In some embodiments, the system can be configured to identify and/orcharacterize different types and/or regions of aortic atherosclerosis orplaque, for example based on density, absolute density, materialdensity, relative density, and/or radiodensity. In some embodiments, thesystem can be configured to characterize a region of aortic plaque intoone or more sub-types of plaque. For example, in some embodiments, thesystem can be configured to characterize a region of plaque as one ormore of low density non-calcified plaque, non-calcified plaque, orcalcified plaque. In some embodiments, calcified plaque can correspondto plaque having a highest density range, low density non-calcifiedplaque can correspond to plaque having a lowest density range, andnon-calcified plaque can correspond to plaque having a density rangebetween calcified plaque and low density non-calcified plaque. Forexample, in some embodiments, the system can be configured tocharacterize a particular region of plaque as low density non-calcifiedplaque when the radiodensity of an image pixel or voxel corresponding tothat region of plaque is between about -189 and about 30 Hounsfieldunits (HU). In some embodiments, the system can be configured tocharacterize a particular region of plaque as non-calcified plaque whenthe radiodensity of an image pixel or voxel corresponding to that regionof plaque is between about 31 and about 350 HU. In some embodiments, thesystem can be configured to characterize a particular region of plaqueas calcified plaque when the radiodensity of an image pixel or voxelcorresponding to that region of plaque is between about 351 and about2500 HU.

In some embodiments, the lower and/or upper Hounsfield unit boundarythreshold for determining whether an aortic plaque corresponds to one ormore of low density non-calcified plaque, non-calcified plaque, and/orcalcified plaque can be about -1000 HU, about -900 HU, about -800 HU,about -700 HU, about -600 HU, about -500 HU, about -400 HU, about -300HU, about -200 HU, about -190 HU, about -180 HU, about -170 HU, about-160 HU, about -150 HU, about -140 HU, about -130 HU, about -120 HU,about -110 HU, about -100 HU, about -90HU, about -80 HU, about -70 HU,about -60 HU, about -50 HU, about -40 HU, about -30 HU, about -20 HU,about -10 HU, about 0 HU, about 10 HU, about 20 HU, about 30 HU, about40 HU, about 50 HU, about 60 HU, about 70 HU, about 80 HU, about 90 HU,about 100 HU, about 110 HU, about 120 HU, about 130 HU, about 140 HU,about 150 HU, about 160 HU, about 170 HU, about 180 HU, about 190 HU,about 200 HU, about 210 HU, about 220 HU, about 230 HU, about 240 HU,about 250 HU, about 260 HU, about 270 HU, about 280 HU, about 290 HU,about 300 HU, about 310 HU, about 320 HU, about 330 HU, about 340 HU,about 350 HU, about 360 HU, about 370 HU, about 380 HU, about 390 HU,about 400 HU, about 410 HU, about 420 HU, about 430 HU, about 440 HU,about 450 HU, about 460 HU, about 470 HU, about 480 HU, about 490 HU,about 500 HU, about 510 HU, about 520 HU, about 530 HU, about 540 HU,about 550 HU, about 560 HU, about 570 HU, about 580 HU, about 590 HU,about 600 HU, about 700 HU, about 800 HU, about 900 HU, about 1000 HU,about 1100 HU, about 1200 HU, about 1300 HU, about 1400 HU, about 1500HU, about 1600 HU, about 1700 HU, about 1800 HU, about 1900 HU, about2000 HU, about 2100 HU, about 2200 HU, about 2300 HU, about 2400 HU,about 2500 HU, about 2600 HU, about 2700 HU, about 2800 HU, about 2900HU, about 3000 HU, about 3100 HU, about 3200 HU, about 3300 HU, about3400 HU, about 3500 HU, and/or about 4000 HU.

In some embodiments, the system can be configured to determine and/orcharacterize the burden of aortic atherosclerosis based at least part onvolume of plaque. In some embodiments, the system can be configured toanalyze and/or determine total volume of aortic plaque and/or volume oflow-density non-calcified plaque, non-calcified plaque, and/or calcifiedplaque in the analyzed portion of the aorta. In some embodiments, thesystem can be configured to perform phenotyping of aorticatherosclerosis by determining a ratio of one or more of the foregoingvolumes of plaque, for example within a portion of the aorta, lesion,vessel, and/or the like.

In some embodiments, the system can be configured to analyze theprogression of aortic atherosclerosis. For example, in some embodiments,the system can be configured to analyze the progression of one or moreparticular regions of plaque and/or overall progression and/or lesionand/or artery-specific progression of plaque. In some embodiments, inorder to analyze the progression of plaque, the system can be configuredto analyze one or more serial images of the subject for phenotypingatherosclerosis. In some embodiments, tracking the progression of plaquecan comprise analyzing changes and/or lack thereof in total plaquevolume and/or volume of low-density non-calcified plaque, non-calcifiedplaque, and/or calcified plaque. In some embodiments, tracking theprogression of plaque can comprise analyzing changes and/or lack thereofin density of a particular region of plaque and/or globally.

In some embodiments, at block 2816, the system can be configured todetermine a risk of MACE and/or arterial disease, such as PAD, based atleast in part on the results of aortic atherosclerosis analysis and/orquantified phenotyping. In determining risk of MACE and/or arterialdisease, in some embodiments, the system can be configured to access oneor more reference values of quantified phenotyping and/or other analysesof aortic atherosclerosis as compared to risk of MACE or arterialdisease, which can be stored on an aortic atherosclerosis risk database2818. In some embodiments, the one or more reference values ofquantified phenotyping and/or other analyses of aortic atherosclerosisas compared to risk of MACE or arterial disease can be derived from apopulation with varying states of aortic atherosclerosis as compared torisk of MACE and/or arterial disease. In some embodiments, the aorticplaque risk database 2818 can be locally accessible by the system and/orcan be located remotely and accessible through a network connection. Insome embodiments, the system can be configured to utilize one or moreartificial intelligence (AI) and/or machine learning (ML) algorithms toautomatically and/or dynamically determine risk of MACE or arterialdisease based on aortic plaque analysis.

In some embodiments, at block 2810, the system can be configured toidentify, analyze, and/or quantify emphysema. In some embodiments, thesystem can be configured to perform quantified phenotyping of emphysema.For example, in some embodiments, the quantitative phenotyping can be ofemphysema burden, volume, type, composition, and/or rate of progressionfor the individual or patient. In some embodiments, the system can beconfigured to utilize one or more image processing, artificialintelligence (AI), and/or machine learning (ML) algorithms toautomatically and/or dynamically perform quantitative phenotyping ofemphysema. For example, in some embodiments, the system can beconfigured to automatically and/or dynamically identify one or morepixels corresponding to emphysema and/or different levels of emphysemaand/or risk thereof for quantitative phenotyping.

In some embodiments, the system can be configured to identify and/orcharacterize different types and/or regions and/or risk levels ofemphysema, for example based on density, absolute density, materialdensity, relative density, and/or radiodensity. For example, the systemcan be configured to ascertain different risk levels of emphysema basedat least in part on the darkness and/or brightness of pixelscorresponding to areas of the lungs, wherein a darker pixel canrepresent a higher risk of emphysema.

In some embodiments, the system can be configured to utilize one or moreHounsfield unit thresholds for characterizing different risk levels ofemphysema. For example, in some embodiments, the system can beconfigured to identify one or more pixels of the lungs of a subject ascorresponding to emphysema and/or a particular type or risk of emphysemawhen the Hounsfield unit is above, below, and/or between one or more ofthe following Hounsfield units: about -1500 HU, about -1400 HU, about-1300 HU, about -1200 HU, about -1100 HU, about -1000 HU, about -990 HU,about -980 HU, about -970 HU, about -960 HU, about -950 HU, about -940HU, about -930 HU, about -920 HU, about -910 HU, about -900 HU, about-800 HU, about -700 HU, about -600 HU, and/or about -500 HU.

In some embodiments, the system can be configured to determine and/orcharacterize the burden of emphysema based at least part on volume ofemphysema. In some embodiments, the system can be configured to analyzeand/or determine total volume of emphysema and/or volume of particularrisk level of emphysema.

In some embodiments, the system can be configured to analyze theprogression of emphysema. For example, in some embodiments, the systemcan be configured to analyze the progression of one or more particularregions of emphysema and/or overall progression of emphysema. In someembodiments, in order to analyze the progression of emphysema, thesystem can be configured to analyze one or more serial images of thesubject for phenotyping emphysema. In some embodiments, tracking theprogression of emphysema can comprise analyzing changes and/or lackthereof in total emphysema volume and/or volume of a particularrisk-level of emphysema. In some embodiments, tracking the progressionof emphysema can comprise analyzing changes and/or lack thereof indensity of a particular region of emphysema and/or globally.

In some embodiments, at block 2820, the system can be configured todetermine a risk of MACE and/or arterial disease, such as PAD, based atleast in part on the results of emphysema analysis and/or quantifiedphenotyping. In determining risk of MACE and/or arterial disease, insome embodiments, the system can be configured to access one or morereference values of quantified phenotyping and/or other analyses ofemphysema as compared to risk of MACE or arterial disease, which can bestored on an emphysema risk database 2822. In some embodiments, the oneor more reference values of quantified phenotyping and/or other analysesof emphysema as compared to risk of MACE or arterial disease can bederived from a population with varying states of emphysema as comparedto risk of MACE and/or arterial disease. In some embodiments, theemphysema risk database 2822 can be locally accessible by the systemand/or can be located remotely and accessible through a networkconnection. In some embodiments, the system can be configured to utilizeone or more artificial intelligence (AI) and/or machine learning (ML)algorithms to automatically and/or dynamically determine risk of MACE orarterial disease based on emphysema analysis.

In some embodiments, at block 2824, the system can be configured togenerate a weighted measure of one or more determined risk levels ofMACE and/or arterial disease, such as PAD. For example, in someembodiments, the system can be configured to generate a weighted measureof risk levels of MACE and/or arterial disease derived from analysis ofone or more of coronary atherosclerosis, aortic atherosclerosis, and/oremphysema. In some embodiments, the system can be configured to weightone or more individually derived risk levels of MACE and/or arterialdisease the same or differently, for example between 0 and 100%. Forexample, in some embodiments, the system can be configured to weight aparticular MACE and/or arterial disease risk level derived from one ofcoronary atherosclerosis, aortic atherosclerosis, and emphysema 100%while discounting the other two.

In some embodiments, at block 2826, the system can be configured todetermine a subject-level multifactor risk of MACE and/or arterialdisease, such as PAD. For example, in some embodiments, in determiningthe subject-level multifactor risk of MACE and/or arterial disease, thesystem can be configured to access one or more reference values ofweighted measures of one or more MACE and/or arterial disease and/or PADrisks, which can be stored on a subject-level MACE or arterial diseaserisk database 2828. In some embodiments, the one or more referencevalues of weighted measures of one or more MACE and/or arterial diseaserisks can be derived from a population with varying levels of risk ofMACE and/or arterial disease, such as PAD. In some embodiments, thesubject-level MACE or arterial disease risk database 2828 can be locallyaccessible by the system and/or can be located remotely and accessiblethrough a network connection. In some embodiments, the system can beconfigured to utilize one or more artificial intelligence (AI) and/ormachine learning (ML) algorithms to automatically and/or dynamicallydetermine a subject-level multifactor risk of MACE or arterial disease,such as PAD.

In some embodiments, at block 2830, the system can be configured todetermine a proposed treatment for the subject based on the determinedsubject-level multifactor risk of MACE or arterial disease, such as PAD.For example, in some embodiments, the proposed treatment can include oneor more of lifestyle change, exercise, diet, medication, and/or invasiveprocedure. In some embodiments, in determining a proposed treatment forthe subject, the system can be configured to access one or morereference treatments previously utilized for subjects with varyinglevels of subject-level multifactor risks of MACE or arterial disease,which can be stored on a treatment database 2832. In some embodiments,the one or more reference treatments can be derived from a populationwith varying levels of subject-level multifactor risks of MACE orarterial disease, such as PAD. In some embodiments, the treatmentdatabase 2832 can be locally accessible by the system and/or can belocated remotely and accessible through a network connection. In someembodiments, the system can be configured to utilize one or moreartificial intelligence (AI) and/or machine learning (ML) algorithms toautomatically and/or dynamically determine a proposed treatment for asubject based on a determined subject-level multifactor risk of MACE orarterial disease, such as PAD.

In some embodiments, at block 2834, the system can be configured togenerate a graphical representation and/or report presenting one or morefindings and/or analyses described herein in connection with FIG. 28C.For example, in some embodiments, the system can be configured togenerate or cause generation of a display presenting results ofquantified phenotyping and/or other analysis of coronary atherosclerosisand/or determined risk of MACE and/or arterial disease, such as PAD,based on the same. In some embodiments, the system can be configured togenerate or cause generation of a display presenting results ofquantified phenotyping and/or other analysis of aortic atherosclerosisand/or determined risk of MACE and/or arterial disease, such as PAD,based on the same. Further, in some embodiments, the system can beconfigured to generate or cause generation of a display presentingresults of quantified phenotyping and/or other analysis of emphysemaand/or determined risk of MACE and/or arterial disease, such as PAD,based on the same. In some embodiments, the system can be configured togenerate or cause generation of a display presenting results of agenerated weighted measure of risk of MACE or arterial disease based onone or more individual analyses, subject-level multifactor risk of MACEor arterial disease, and/or a proposed treatment for a subject.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 2802-2834, for example for oneor more other vessels, segment, regions of plaque, different subjects,and/or for the same subject at a different time. As such, in someembodiments, the system can provide for longitudinal tracking of risk ofMACE or arterial disease and/or personalized treatment for a subject.

FIG. 28D is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for image-based diagnosis, riskassessment, and/or characterization of a major adverse cardiovascularevent. As illustrated in FIG. 28D, in some embodiments, the system canbe configured to access one or more medical images and identify,analyze, and/or quantify one or more of coronary atherosclerosis, aorticatherosclerosis, and/or emphysema utilizing one or more processes and/orfeatures described above in relation to FIG. 28C, such as in connectionwith blocks 2802, 2804, 2806, 2808, and/or 2810.

In contrast to the embodiments described in FIG. 28C, however, in someembodiments as illustrated in FIG. 28D, the system can be configured togenerate a weighted measure of one or more analysis results of coronaryatherosclerosis, aortic atherosclerosis, and/or emphysema at block 2836which can be used to directly determine a subject-level multifactor riskof MACE or arterial disease, such as PAD, at block 2838, as opposed todetermining individual risk levels of MACE or arterial disease based ona single factor of coronary atherosclerosis, aortic atherosclerosis,and/or emphysema. In particular, in some embodiments, the system can beconfigured to weight one or more analysis results of coronaryatherosclerosis, aortic atherosclerosis, and/or emphysema the same ordifferently, for example between 0 and 100%. For example, in someembodiments, the system can be configured to weight the analysis resultsof one of coronary atherosclerosis, aortic atherosclerosis, and/oremphysema more due to predicted accuracy levels of one over another,while discounting others.

In some embodiments, at block 2838, the system can be configured todetermine a subject-level multifactor risk of MACE and/or arterialdisease, such as PAD, based on the generated weighted measure of one ormore analysis results of coronary atherosclerosis, aorticatherosclerosis, and/or emphysema. For example, in some embodiments, indetermining the subject-level multifactor risk of MACE and/or arterialdisease, the system can be configured to access one or more referencevalues of weighted measures of analysis results of coronaryatherosclerosis, aortic atherosclerosis, and/or emphysema, which can bestored on a subject-level MACE or arterial disease risk database 2840.In some embodiments, the one or more reference values of weightedmeasures of analysis results of coronary atherosclerosis, aorticatherosclerosis, and/or emphysema can be derived from a population withvarying levels of risk of MACE and/or arterial disease. In someembodiments, the subject-level MACE or arterial disease risk database2840 can be locally accessible by the system and/or can be locatedremotely and accessible through a network connection. In someembodiments, the system can be configured to utilize one or moreartificial intelligence (AI) and/or machine learning (ML) algorithms toautomatically and/or dynamically determine a subject-level multifactorrisk of MACE or arterial disease risk of MACE or arterial disease.

In some embodiments, the system can further be configured to determine aproposed treatment and/or generate a graphical representation or reportas discussed herein in connection with blocks 2830, 2832, and 2834. Insome embodiments, the system can be configured to repeat one or moreprocesses described in relation to FIG. 28D, for example for one or moreother vessels, segment, regions of plaque, different subjects, and/orfor the same subject at a different time. As such, in some embodiments,the system can provide for longitudinal tracking of risk of MACE orarterial disease and/or personalized treatment for a subject.

FIG. 28E is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for image-based diagnosis, riskassessment, and/or characterization of a major adverse cardiovascularevent. As illustrated in FIG. 28E, in some embodiments, the system canbe configured to utilize one or more databases or datasets comprising aplurality of predetermined diagnoses, medical conditions, risk scores,and/or candidate treatments to effectively transform results ofquantified phenotyping based on a medical image to a risk score and/orcandidate treatments. For example, in some embodiments, the system canbe configured to automatically and/or dynamically perform quantifiedphenotyping of a medical image to analyze coronary atherosclerosis,aortic atherosclerosis, and/or emphysema and/or utilize the results ofsuch quantified phenotyping to determine a health risk assessment of thesubject and/or one or more candidate treatments. In order to facilitateeffective transformation of such quantified phenotyping results to ahealth risk assessment and/or one or more candidate treatments, thesystem can be configured to utilize one or more such databases and/ordatasets. By utilizing such databases and/or datasets, in someembodiments, the system can be configured to efficiently process theresults of quantified phenotyping in a repeatable and/or automatedmanner, thereby saving computing resources and/or need for humanintervention. As such, in some embodiments, the system can be configuredto automatically and/or dynamically analyze a medical image, performquantified phenotyping, and process the results through an automatedtriage process to automatically assess a health risk and/or treatmentfor a subject.

More specifically, in some embodiments, at block 2802, the system can beconfigured to access one or more medical images, for example from amedical image database 2804 as discussed above in relation to FIGS.28C-28D.

In some embodiments, at block 2842, the system can be configured toanalyze the one or more medical images to perform phenotyping, such asquantified phenotyping. In particular, in some embodiments, the systemcan be configured to identify one or more regions of interest forphenotyping, such as for example one or more portions of the coronaryarteries, aortic arteries, and/or lungs of the subject. In someembodiments, the system can be configured to perform quantifiedphenotyping of one or more of coronary atherosclerosis, aorticatherosclerosis, and/or emphysema, for example utilizing one or moreprocesses described herein in relation to FIGS. 28C-28D.

In some embodiments, based on results of the quantified phenotyping, thesystem at block 2844 can be configured to determine if a correspondingdiagnosis exists in a database or dataset of predetermined diagnoses2846. In some embodiments, in order to efficiently and/or effectivelydisregard healthy subjects, the predetermined diagnoses can correspondonly to a subset of quantified phenotyping results. In other words, insome embodiments, not all quantified phenotyping results may correspondto a predetermined diagnosis. In some embodiments, if the quantifiedphenotyping result does not correspond to a predetermined diagnosis, theprocess can then be completed, as no further analysis is warranted. Incontrast, if a corresponding preset or predetermined diagnosis is foundto exist for the quantified phenotyping results, then the system can beconfigured to further analyze the results.

In some embodiments, if a corresponding predetermined diagnosis is foundto exist for the quantified phenotyping results, the system at block2848 can be configured to determine if a corresponding medical conditionexists in a database or dataset of predetermined medical conditions2850. In some embodiments, in order to efficiently and/or effectivelydisregard healthy subjects, the predetermined medical conditions cancorrespond only to a subset of predetermined diagnoses. In other words,in some embodiments, not all predetermined diagnoses may correspond to apredetermined medical condition. In some embodiments, if the diagnosisderived from quantified phenotyping does not correspond to apredetermined medical condition, the process can then be completed, asno further analysis is warranted. In contrast, if a corresponding presetor predetermined medical condition is found to exist for the diagnosisderived from the quantified phenotyping results, then the system can beconfigured to further analyze the results.

In some embodiments, if a corresponding predetermined medical conditionis found to exist, the system at block 2852 can be configured todetermine a health risk score for the subject, for example by accessinga risk database 2854. The risk database 2854 can comprise one or moredifferent risk levels and/or scores corresponding to differentpredetermined medical conditions.

In some embodiments, the system can be configured to determine one ormore proposed and/or candidate treatments for the subject at block 2830,for example utilizing one or more treatments stored on a treatmentdatabase 2832, as described in more detail in relation to FIGS. 28C-28D.In some embodiments, the system can be configured to generate agraphical representation and/or report at block 2834, for exampledisplaying the results of one or more of the quantified phenotyping,corresponding diagnosis, corresponding medical condition, determinedrisk score, and/or proposed or candidate treatment(s), as described inmore detail in relation to FIGS. 28C-28D.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 2802-2854, for example for oneor more other vessels, segment, regions of plaque, different subjects,and/or for the same subject at a different time. As such, in someembodiments, the system can provide for longitudinal tracking of asubject’s health risk derived automatically from quantified phenotypingof serial medical images and utilizing one or more predetermineddatasets of diagnoses, medical conditions, and/or risk scores forefficient and/or effective processing.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 28F. The example computer system 2872 is incommunication with one or more computing systems 2890 and/or one or moredata sources 2892 via one or more networks 2888. While FIG. 28Fillustrates an embodiment of a computing system 2872, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 2872 can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 2872 can comprise a Risk Assessment Module 2884 thatcarries out the functions, methods, acts, and/or processes describedherein. The Risk Assessment Module 2884 executed on the computer system2872 by a central processing unit 2876 discussed further below.

In general the word “module,” as used herein, refers to logic embodiedin hardware or firmware or to a collection of software instructions,having entry and exit points. Modules are written in a program language,such as JAVA, C, or C++, or the like. Software modules can be compiledor linked into an executable program, installed in a dynamic linklibrary, or can be written in an interpreted language such as BASIC,PERL, LAU, PHP or Python and any such languages. Software modules can becalled from other modules or from themselves, and/or can be invoked inresponse to detected events or interruptions. Modules implemented inhardware include connected logic units such as gates and flip-flops,and/or can include programmable units, such as programmable gate arraysor processors.

Generally, the modules described herein refer to logical modules thatcan be combined with other modules or divided into sub-modules despitetheir physical organization or storage. The modules are executed by oneor more computing systems, and can be stored on or within any suitablecomputer readable medium, or implemented in-whole or in-part withinspecial designed hardware or firmware. Not all calculations, analysis,and/or optimization require the use of computer systems, though any ofthe above-described methods, calculations, processes, or analyses can befacilitated through the use of computers. Further, in some embodiments,process blocks described herein can be altered, rearranged, combined,and/or omitted.

The computer system 2872 includes one or more processing units (CPU)2876, which can comprise a microprocessor. The computer system 2872further includes a physical memory 2880, such as random access memory(RAM) for temporary storage of information, a read only memory (ROM) forpermanent storage of information, and a mass storage device 2874, suchas a backing store, hard drive, rotating magnetic disks, solid statedisks (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory,diskette, or optical media storage device. Alternatively, the massstorage device can be implemented in an array of servers. Typically, thecomponents of the computer system 2872 are connected to the computerusing a standards based bus system. The bus system can be implementedusing various protocols, such as Peripheral Component Interconnect(PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) andExtended ISA (EISA) architectures.

The computer system 2872 includes one or more input/output (I/O) devicesand interfaces 2882, such as a keyboard, mouse, touch pad, and printer.The I/O devices and interfaces 2882 can include one or more displaydevices, such as a monitor, that allows the visual presentation of datato a user. More particularly, a display device provides for thepresentation of GUIs as application software data, and multi-mediapresentations, for example. The I/O devices and interfaces 2882 can alsoprovide a communications interface to various external devices. Thecomputer system 2872 can comprise one or more multi-media devices 2878,such as speakers, video cards, graphics accelerators, and microphones,for example.

Computing System Device / Operating System

The computer system 2872 can run on a variety of computing devices, suchas a server, a Windows server, a Structure Query Language server, a UnixServer, a personal computer, a laptop computer, and so forth. In otherembodiments, the computer system 2872 can run on a cluster computersystem, a mainframe computer system and/or other computing systemsuitable for controlling and/or communicating with large databases,performing high volume transaction processing, and generating reportsfrom large databases. The computing system 2872 is generally controlledand coordinated by an operating system software, such as z/OS, Windows,Linux, UNIX, BSD, PHP, SunOS, Solaris, MacOS, ICloud services or othercompatible operating systems, including proprietary operating systems.Operating systems control and schedule computer processes for execution,perform memory management, provide file system, networking, and I/Oservices, and provide a user interface, such as a graphical userinterface (GUI), among other things.

Network

The computer system 2872 illustrated in FIG. 28F is coupled to a network2888, such as a LAN, WAN, or the Internet via a communication link 2886(wired, wireless, or a combination thereof). Network 2888 communicateswith various computing devices and/or other electronic devices. Network2888 is communicating with one or more computing systems 2890 and one ormore data sources 2892. The Risk Assessment Module 2884 can access orcan be accessed by computing systems 2890 and/or data sources 2892through a web-enabled user access point. Connections can be a directphysical connection, a virtual connection, and other connection type.The web-enabled user access point can comprise a browser module thatuses text, graphics, audio, video, and other media to present data andto allow interaction with data via the network 2888.

The output module can be implemented as a combination of an all-pointsaddressable display such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. The output module can be implemented to communicate with inputdevices 2882 and they also include software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements, such as menus, windows, dialogue boxes, tool bars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the output module can communicate with a set ofinput and output devices to receive signals from the user.

Other Systems

The computing system 2872 can include one or more internal and/orexternal data sources (for example, data sources 2892). In someembodiments, one or more of the data repositories and the data sourcesdescribed above can be implemented using a relational database, such asDB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well asother types of databases such as a flat-file database, an entityrelationship database, and object-oriented database, and/or arecord-based database.

The computer system 2872 can also access one or more databases 2892. Thedatabases 2892 can be stored in a database or data repository. Thecomputer system 2872 can access the one or more databases 2892 through anetwork 2888 or can directly access the database or data repositorythrough I/O devices and interfaces 2882. The data repository storing theone or more databases 2892 can reside within the computer system 2872.

URLs and Cookies

In some embodiments, one or more features of the systems, methods, anddevices described herein can utilize a URL and/or cookies, for examplefor storing and/or transmitting data or user information. A UniformResource Locator (URL) can include a web address and/or a reference to aweb resource that is stored on a database and/or a server. The URL canspecify the location of the resource on a computer and/or a computernetwork. The URL can include a mechanism to retrieve the networkresource. The source of the network resource can receive a URL, identifythe location of the web resource, and transmit the web resource back tothe requestor. A URL can be converted to an IP address, and a Doman NameSystem (DNS) can look up the URL and its corresponding IP address. URLscan be references to web pages, file transfers, emails, databaseaccesses, and other applications. The URLs can include a sequence ofcharacters that identify a path, domain name, a file extension, a hostname, a query, a fragment, scheme, a protocol identifier, a port number,a username, a password, a flag, an object, a resource name and/or thelike. The systems disclosed herein can generate, receive, transmit,apply, parse, serialize, render, and/or perform an action on a URL.

A cookie, also referred to as an HTTP cookie, a web cookie, an internetcookie, and a browser cookie, can include data sent from a websiteand/or stored on a user’s computer. This data can be stored by a user’sweb browser while the user is browsing. The cookies can include usefulinformation for websites to remember prior browsing information, such asa shopping cart on an online store, clicking of buttons, logininformation, and/or records of web pages or network resources visited inthe past. Cookies can also include information that the user enters,such as names, addresses, passwords, credit card information, etc.Cookies can also perform computer functions. For example, authenticationcookies can be used by applications (for example, a web browser) toidentify whether the user is already logged in (for example, to a website). The cookie data can be encrypted to provide security for theconsumer. Tracking cookies can be used to compile historical browsinghistories of individuals. Systems disclosed herein can generate and usecookies to access data of an individual. Systems can also generate anduse JSON web tokens to store authenticity information, HTTPauthentication as authentication protocols, IP addresses to tracksession or identity information, URLs, and the like.

Examples of Embodiments Relating to Automatically Determining aDiagnosis, Risk Assessment, and Characterization of Heart Disease

The following are non-limiting examples of certain embodiments ofsystems and methods for determining a diagnosis, risk assessment, andcharacterization of heart disease and/or other related features. Otherembodiments may include one or more other features, or differentfeatures, that are discussed herein.

Embodiment 1: A computer-implemented method of facilitating assessmentof risk of heart disease for a subject based on multi-dimensionalinformation derived from non-invasive medical image analysis, the methodcomprising: accessing, by a computer system, one or more medical imagesof a subject, wherein the medical image of the subject is obtainednon-invasively; analyzing, by the computer system, the one or moremedical images of the subject to identify one or more portions ofcoronary arteries, aorta, and lungs of the subject; identifying, by thecomputer system, one or more regions of plaque in the identified one ormore portions of the coronary arteries; analyzing, by the computersystem, the identified one or more regions of plaque in the coronaryarteries to perform quantified phenotyping of coronary atherosclerosiscomprising total plaque volume, low-density non-calcified plaque volume,non-calcified plaque volume, and calcified plaque volume in the one ormore portions of coronary arteries; identifying, by the computer system,one or more regions of plaque in the identified one or more portions ofthe aorta; analyzing, by the computer system, the identified one or moreregions of plaque in the aorta to perform quantified phenotyping ofaortic atherosclerosis comprising total plaque volume, low-densitynon-calcified plaque volume, non-calcified plaque volume, and calcifiedplaque volume in the one or more portions of the aorta; analyzing, bythe computer system, the identified one or more portions of the lungs ofthe subject to determine presence or state of emphysema; and causing, bythe computer system, display of a graphical representation comprisingresults of the quantified phenotyping of coronary atherosclerosis,results of the quantified phenotyping of aortic atherosclerosis, andpresence or state of emphysema to facilitate assessment of risk of heartdisease for the subject based on multidimensional analysis of coronaryatherosclerosis, aortic atherosclerosis, and emphysema, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereinthe one or more medical images comprises a single medical image showingthe one or more portions of the coronary arteries, aorta, and lungsappear on a single medical image.

Embodiment 3: The computer-implemented method of Embodiments 1 or 2,wherein the one or more medical images comprises a plurality of medicalimages.

Embodiment 4: The computer-implemented method of any one of Embodiments1 to 3, wherein one or more of the quantitative phenotyping of coronaryatherosclerosis or the quantitative phenotyping of aorticatherosclerosis is performed based at least in part on analysis ofdensity values of one or more pixels of the one or more medical imagescorresponding to plaque.

Embodiment 5: The computer-implemented method of Embodiment 4, whereinthe density values comprise radiodensity values.

Embodiment 6: The computer-implemented method of any one of Embodiments1 to 5, wherein the presence or state of emphysema is determined basedat least in part on analysis of density values of one or more pixels ofthe one or more medical images corresponding to the one or more portionsof the lungs.

Embodiment 7: The computer-implemented method of Embodiment 6, whereinthe density values comprise radiodensity values.

Embodiment 8: The computer-implemented method of any one of Embodiments1 to 7, wherein the one or more regions of plaque are identified as lowdensity non-calcified plaque when a radiodensity value is between about-189 and about 30 Hounsfield units.

Embodiment 9: The computer-implemented method of any one of Embodiments1 to 8, wherein the one or more regions of plaque are identified asnon-calcified plaque when a radiodensity value is between about 30 andabout 350 Hounsfield units.

Embodiment 10: The computer-implemented method of any one of Embodiments1 to 9, wherein the one or more regions of plaque are identified ascalcified plaque when a radiodensity value is between about 351 and 2500Hounsfield units.

Embodiment 11: The computer-implemented method of any one of Embodiments1 to 10, wherein the one or more medical images comprise a ComputedTomography (CT) image.

Embodiment 12: The computer-implemented method of any one of Embodiments1 to 11, wherein the one or more medical images are obtained using animaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, MR imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS).

Embodiment 13: The computer-implemented method of any one of Embodiments1 to 12, further comprising generating, by the computer system, amultifactor assessment of risk of heart disease for the subject based atleast in part on analysis of coronary atherosclerosis, aorticatherosclerosis, and emphysema.

Embodiment 14: The computer-implemented method of any one of Embodiments1 to 13, wherein the assessment of risk of heart disease is generatedutilizing a machine learning algorithm.

Embodiment 15: The computer-implemented method of any one of Embodiments1 to 14, further comprising generating, by the computer system, arecommended treatment for the subject based at least in part on thegenerated assessment of risk of heart disease for the subject.

Embodiment 16: The computer-implemented method of Embodiment 13, whereinthe assessment of risk of heart disease is generated at least in partby: comparing results of the quantified phenotyping of coronaryatherosclerosis to a set of reference values of quantified phenotypingof coronary atherosclerosis corresponding to different levels of risk ofheart disease; comparing results of the quantified phenotyping of aorticatherosclerosis to a set of reference values of quantified phenotypingof aortic atherosclerosis corresponding to different levels of risk ofheart disease; and comparing the presence or state of emphysema to a setof reference values of state of emphysema corresponding to differentlevels of risk of heart disease.

Embodiment 17: The computer-implemented method of Embodiment 16, whereinone or more of the set of reference values of quantified phenotyping ofcoronary atherosclerosis, set of reference values of quantifiedphenotyping of aortic atherosclerosis, or set of reference values ofstate of emphysema is derived from a reference population with varyinglevels of risk of heart disease.

Embodiment 18: The computer-implemented method of any one of Embodiments1 to 17, wherein the reference population is selected based on one ormore of age, gender, or ethnicity of the subject.

Embodiment 19: The computer-implemented method of any one of Embodiments1 to 13, wherein the assessment of risk of heart disease is generated atleast in part by: assessing risk of heart disease based on the resultsof quantified phenotyping of coronary atherosclerosis; assessing risk ofheart disease based on the results of the quantified phenotyping ofaortic atherosclerosis; assessing risk of heart disease based on thepresence or state of emphysema; generating a weighted measure of therisk of heart disease assessed based on the results of quantifiedphenotyping of coronary atherosclerosis, the results of the quantifiedphenotyping of aortic atherosclerosis, and the presence or state ofemphysema; and generating the multifactor assessment of heart diseasebased on the weighted measure.

Embodiment 20: A computer-implemented method of assessing risk of heartdisease for a subject based on multi-dimensional information derivedfrom non-invasive medical image analysis, the method comprising:accessing, by a computer system, results of quantified phenotyping ofcoronary atherosclerosis of a subject at a first point in time, thequantified phenotyping of coronary atherosclerosis comprising totalplaque volume, low-density non-calcified plaque volume, non-calcifiedplaque volume, and calcified plaque volume in one or more portions ofcoronary arteries of the subject; accessing, by a computer system,results of quantified phenotyping of aortic atherosclerosis of thesubject at the first point in time, the quantified phenotyping of aorticatherosclerosis comprising total plaque volume, low-densitynon-calcified plaque volume, non-calcified plaque volume, and calcifiedplaque volume in one or more portions of the aorta of the subject;accessing, by the computer system, a medical image of the subject,wherein the medical image of the subject is obtained at a second pointin time, the medical image comprising the one or more portions ofcoronary arteries and the one or more portions of the aorta of thesubject; performing, by the computer system, quantitative phenotyping ofcoronary atherosclerosis at the second point in time; performing, by thecomputer system, quantitative phenotyping of aortic atherosclerosis atthe second point in time; analyzing, by the computer system, progressionof coronary atherosclerosis based at least in part on comparing theresults of quantitative phenotyping of coronary atherosclerosis betweenthe first point in time and the second point in time; analyzing, by thecomputer system, progression of aortic atherosclerosis based at least inpart on comparing the results of quantitative phenotyping of aorticatherosclerosis between the first point in time and the second point intime; and assessing, by the computer system, a risk of heart disease forthe subject based at least in part on the analysis of the progression ofcoronary atherosclerosis and the progression of aortic atherosclerosis,wherein the computer system comprises a computer processor and anelectronic storage medium.

Embodiment 21: The computer-implemented method of Embodiment 20, whereinthe risk of heart disease for the subject is assessed to be high whenthe volume of non-calcified plaque in one or more of the coronaryarteries or aorta is higher at the second point in time than at thefirst point in time.

Embodiment 22: The computer-implemented method of any one of Embodiments20 or 21, wherein the risk of heart disease for the subject is assessedto be high when the total plaque volume in one or more of the coronaryarteries or aorta is higher at the second point in time than at thefirst point in time.

Embodiment 23: The computer-implemented method of any one of Embodiments20 to 22, wherein the risk of heart disease for the subject is assessedto be high when the subject was non-responsive to a medicationprescribed to the subject at the first point in time to stabilizeatherosclerosis.

Embodiment 24: A computer-implemented method of assessing risk of heartdisease for a subject based on multi-dimensional information derivedfrom non-invasive medical image analysis, the method comprising:accessing, by a computer system, results of quantified phenotyping ofcoronary atherosclerosis of a subject at a first point in time, thequantified phenotyping of coronary atherosclerosis comprising totalplaque volume, low-density non-calcified plaque volume, non-calcifiedplaque volume, and calcified plaque volume in one or more portions ofcoronary arteries of the subject; accessing, by a computer system, astate of emphysema of the subject analyzed at the first point in time;accessing, by the computer system, a medical image of the subject,wherein the medical image of the subject is obtained at a second pointin time, the medical image comprising the one or more portions ofcoronary arteries and lungs of the subject; analyzing, by the computersystem, the medical image to perform quantitative phenotyping ofcoronary atherosclerosis at the second point in time; analyzing, by thecomputer system, the medical image to determine a state of emphysema atthe second point in time; analyzing, by the computer system, progressionof coronary atherosclerosis based at least in part on comparing theresults of quantitative phenotyping of coronary atherosclerosis betweenthe first point in time and the second point in time; analyzing, by thecomputer system, progression of emphysema based at least in part oncomparing the state of emphysema between the first point in time and thesecond point in time; and assessing, by the computer system, a risk ofheart disease for the subject based at least in part on the analysis ofthe progression of coronary atherosclerosis and the progression ofemphysema, wherein the computer system comprises a computer processorand an electronic storage medium.

Embodiment 25: The computer-implemented method of Embodiment 24, whereinthe risk of heart disease for the subject is assessed to be high whenthe volume of non-calcified plaque in the one or more portions ofcoronary arteries is higher at the second point in time than at thefirst point in time.

Embodiment 26: The computer-implemented method of any one of Embodiments24 to 25, wherein the risk of heart disease for the subject is assessedto be high when the total plaque volume in the one or more portions ofcoronary arteries is higher at the second point in time than at thefirst point in time.

Embodiment 27: The computer-implemented method of any one of Embodiments24 to 26, wherein the risk of heart disease for the subject is assessedto be high when the subject was non-responsive to a medicationprescribed to the subject at the first point in time to stabilizeatherosclerosis.

Embodiment 28: A computer-implemented method of assessing risk ofperipheral artery disease (PAD) for a subject based on multi-dimensionalinformation derived from non-invasive medical image analysis, the methodcomprising: accessing, by a computer system, one or more medical imagesof a subject, wherein the medical image of the subject is obtainednon-invasively; analyzing, by the computer system, the one or moremedical images of the subject to identify one or more coronary arteriesof the subject; identifying, by the computer system, one or more regionsof plaque in the identified one or more coronary arteries; analyzing, bythe computer system, the identified one or more regions of plaque in thecoronary arteries to perform quantified phenotyping of coronaryatherosclerosis comprising total plaque volume, low-densitynon-calcified plaque volume, non-calcified plaque volume, and calcifiedplaque volume in the one or more coronary arteries; comparing, by thecomputer system, results of the quantified phenotyping of coronaryatherosclerosis to a set of reference values of quantified phenotypingof coronary atherosclerosis corresponding to different levels of risk ofPAD; and generating, by the computer system, an assessment of risk ofPAD for the subject based at least in part on the comparison of theresults of the quantified phenotyping of coronary atherosclerosis to theset of reference values, wherein the computer system comprises acomputer processor and an electronic storage medium.

Embodiment 29: The computer-implemented method of Embodiment 28, furthercomprising: identifying, by the computer system, one or more portions ofthe aorta of the subject on the medical image; identifying, by thecomputer system, one or more regions of plaque in the identified one ormore portions of the aorta; analyzing, by the computer system, theidentified one or more regions of plaque in the aorta to performquantified phenotyping of aortic atherosclerosis comprising total plaquevolume, low-density non-calcified plaque volume, non-calcified plaquevolume, and calcified plaque volume in the one or more portions of theaorta; and comparing, by the computer system, results of the quantifiedphenotyping of aortic atherosclerosis to a set of reference values ofquantified phenotyping of aortic atherosclerosis corresponding todifferent levels of risk of PAD, wherein the assessment of risk of PADfor the subject is further generated based at least in part on thecomparison of the results of the quantified phenotyping of aorticatherosclerosis to the set of reference values of quantified phenotypingof aortic atherosclerosis.

Embodiment 30: The computer-implemented method of any one of Embodiments28 to 30, further comprising: identifying, by the computer system, oneor more portions of the lungs of the subject on the medical image;analyzing, by the computer system, the identified one or more portionsof the lungs of the subject to determine a state of emphysema for thesubject; and comparing, by the computer system, the determined state ofemphysema for the subject to a set of reference values of states ofemphysema corresponding to different levels of risk of PAD, wherein theassessment of risk of PAD for the subject is further generated based atleast in part on the comparison of the results of the determined stateof emphysema for the subject to the set of reference values of states ofemphysema.

Embodiment 31: A computer-implemented method of assessing a health riskof a subject based on quantitative phenotyping derived from non-invasivemedical image analysis, the method comprising: accessing, by a computersystem, one or more medical images of a subject, wherein the medicalimage of the subject is obtained non-invasively; analyzing, by thecomputer system, the one or more medical images of the subject toidentify one or more regions of interest, the one or more regions ofinterest comprising one or more portions of portions of coronaryarteries, aorta, or lungs of the subject; automatically analyzing, bythe computer system, the one or more regions of interest to performquantified phenotyping, the quantified phenotyping comprising one ormore of coronary atherosclerosis, aortic atherosclerosis, or emphysema;accessing, by the computer system, a first dataset comprising aplurality of predetermined diagnoses to determine presence of anapplicable predetermined diagnosis corresponding to results of thequantified phenotyping; accessing, by the computer system, when anapplicable predetermined diagnosis corresponding to results of thequantified phenotyping is present, a second dataset comprising aplurality of predetermined medical conditions to determine presence ofan applicable predetermined medical condition corresponding to theapplicable predetermined diagnosis; automatically determining, by thecomputer system, when an applicable predetermined medical conditioncorresponding to the applicable predetermined diagnosis is present, athird database comprising a plurality of health risk scores to determinean applicable health risk score for the subject corresponding to theapplicable predetermined medical condition, wherein the applicablehealth risk score is derived from the quantified phenotyping of the oneor more medical images; and determining, by the computer system, one ormore candidate treatments for the subject based on the applicable healthrisk score, wherein the computer system comprises a computer processorand an electronic storage medium.

Embodiment 32: The computer-implemented method of Embodiment 31, furthercomprising causing, by the computer system, generation of a graphicalrepresentation of the determined one or more candidate treatments forthe subject.

Embodiment 33: The computer-implemented method of Embodiments 31 or 32,wherein the quantitative phenotyping is performed based at least in parton analysis of density values of one or more pixels of the one or moremedical images.

Embodiment 34: The computer-implemented method of any one of Embodiments31-33, wherein the density values comprise radiodensity values.

Embodiment 35: The computer-implemented method of any one of Embodiments31-34, wherein the one or more medical images are obtained using animaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, MR imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS).

Improving Accuracy of CAD Measurements

Various embodiments described herein relate to systems, devices, andmethods for improving the accuracy of CAD measurements in non-invasiveimaging. While the primary examples described in this section relate toapproaches for improving the accuracy of CAD measurements bynon-invasive CT angiography, these techniques can be applied to anyimaging modality of any anatomical structure that exhibits motion (orother artifacts) across a series of acquired images. In this way, thefeatures described herein are broadly applicable, and this disclosureshould not be limited to the particular examples described herein.

As an example, in some embodiments, a CT scan is performed of the heart,with multiple “phases” or “series” acquired during the cardiac cycle(e.g., as the heart is contracting or expanding). Each phase or seriescan comprise an image or a plurality of images (e.g., a video) capturedduring a different portion of the cardiac cycle. In some embodiments,the systems, methods, and devices described herein are configured toidentify where, in the different phases or series acquired during thecardiac cycle, the optimal image quality for each artery, branch, orsegment is present.

The phase or series that provides the highest image quality for anyparticular artery, branch, or segment can then be used to performvision-based or other forms of CAD measurement. For example, in someembodiments, the phase or series that provides the highest image qualityfor any particular artery can be analyzed to provide, for example,quantitative phenotyping of atherosclerosis. The quantitativephenotyping of atherosclerosis can include, for example, analysis of oneor more of plaque volume, plaque composition, or plaque progression. Inthis way, the systems, methods, and devices described herein are furtherconfigured to provide the capability to “mix-and-match” these arteriesacross different points in the cardiac cycle to ensure that measurementsof coronary atherosclerosis and vascular morphology are being done onthe images at a “phase” or “series” that represents the ideal imagequality for that particular artery, branch, or segment.

Additionally or alternatively, in some embodiments, the different phasesor series that provide the highest image quality for each of thedifferent arteries, branches, or segments can also be combined into acomposite image that provides improved visualization of the heart.

These features can provide a significant improvement over conventionalimaging and analysis modalities and can provide a solution to one ormore drawbacks associated with the same.

Coronary Computed Tomography Angiography (CCTA) has developed into aclinically useful, guideline-directed non-invasive imaging modality fordiagnosis of coronary artery disease (CAD). Improvements in CTtechnology now enable near motion free images of the coronary arteries,which allows for accurate measurements of atherosclerosis burden andtype, and vascular morphology.

However, CCTA is still susceptible to significant imaging artifacts,owing to such common contributors as coronary artery motion, poorcontrast opacification and beam hardening artifacts. For the firstissue, coronary artery motion, common solutions have been to lowerpatients’ heart rates using oral or intravenous beta blocker medicationsso that the limited temporal resolution of the current generation CTscanners can still produce relatively motion free images. Yet, even withslowing a patient’s heart rate and maximizing temporal resolution onlatest-generation scanners, the different coronary arteries moveunpredictably during the cardiac cycle (e.g., as the heart iscontracting and expanding). Imaging across the cardiac cycle candemonstrate this motion (and its associated motion artifacts) for eachartery and its branches. Often, one artery is visualized with high imagequality at one point of the cardiac cycle, while a different artery isvisualized with high image quality at another point of the cardiaccycle. The same can be observed for contrast enhancement or beamhardening, with image quality differing across the cardiac cycle.

At present, common clinical practice in image interpretation is toselect the “phase” or “series” within the cardiac cycle that overallrepresents the best image quality with motion-free images of the heartarteries. However, this approach may allow for the analysis of themajority of vessels which exhibit ideal image quality, but does notnecessarily allow for analysis of each and every vessel at the point inthe cardiac cycle when it is of highest quality. That is, one artery maybe of ideal image quality in one phase or series, while another arterymay be of ideal image quality in another phase or series. Thisobservation, which is noted for arteries, can also be applied to arterybranches and artery segments. Currently, an imaging physiciancognitively reunites the information of both reconstructions,acquisitions, or series of images and qualitatively make aninterpretation. This is not ideal because it is prone to error, it isqualitatively (and not quantitatively) done, and it is very dependent onthe expertise of the doctor.

To address this need, this application, describes systems, methods, anddevices that are configured to identify optimal image quality on anartery, branch, or segment-by-artery, branch, or segment basis, and thatcan provide the capability to “mix-and-match” these arteries acrossdifferent points in the cardiac cycle to ensure that measurements ofcoronary atherosclerosis and vascular morphology are being done on theimages at the phase or series that represents the ideal image qualityfor that particular artery, branch, or segment.

In some embodiments, the inventions provided herein describe novelapproaches to improving the accuracy of CAD measurements by non-invasiveCT angiography, but this technique can be applied to any imagingmodality of any anatomic structure that exhibits motion (or otherartifacts) across a series of acquired images.

As discussed herein, in some embodiments, the systems, devices, andmethods described herein are configured for improving the accuracy ofCAD measurements in non-invasive imaging. In particular, in someembodiments, a CT scan is performed of the heart, with multiple “phases”or “series” acquired during the cardiac cycle (e.g., as the heart iscontracting or expanding). In some embodiments, the systems, methods,and devices described herein are configured to identify where, in thedifferent phases or series acquired during the cardiac cycle, theoptimal image quality for each artery, branch, or segment is determined.Then, in some embodiments, the systems, methods, and devices describedherein are configured to provide the capability to “mix-and-match” thesearteries across different points in the cardiac cycle to ensure thatmeasurements of coronary atherosclerosis and vascular morphology arebeing done on the images at a “phase” or “series” that represents theideal image quality for that particular artery, branch, or segment.

FIG. 29A is a block diagram illustrating an example embodiment of asystem, device, and method for improving the accuracy of CADmeasurements in non-invasive imaging. As illustrated in FIG. 29A, insome embodiments, the system can receive (e.g., capture or otherwiseacquire) image data of a heart of an individual or patient at block2902. The image can include multiple phases or series acquired duringthe cardiac cycle. For example, the image can include phases or seriesrepresenting the heart as it contracts or expands during the cardiaccycle. Each phase or series can include, for example, a single image ora plurality of images (such as a video). In some embodiments, each phaseor series can correspond to a portion or sub portion of the cardiaccycle.

In some embodiments, the system can be configured to receive the imageof the individual or patient from a medical imaging device. For example,the image can comprise an image obtained by one or more modalities, suchas computed tomography (CT), contrast-enhanced CT, non-contrast CT,x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), MRimaging, optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), and/or near-field infrared spectroscopy (NIRS). Insome embodiments, the image can be stored on and/or received from amedical image database 2914.

In some embodiments, at block 2904, the system can be configured to aanalyze the image data received at block 2902 to label one or more ofthe coronary arteries, branches, or segments of the heart. For example,in some embodiments, an algorithm is developed, validated, and appliedthat is configured to auto-extract and auto-label the coronary arteries,their branches and the coronary segments. In some embodiments, thesystem can be configured to utilize one or more image processing,artificial intelligence (AI), and/or machine learning (ML) algorithms toautomatically and/or dynamically identify and/or label one or morearteries, vessels, and/or a portion thereof within each phase or seriesof the image data.

At block 2906, the system can be configured to identify one or moreanatomical landmarks of the heart that are identifiable across themultiple phases or series of the image. For example, identification ofthese landmarks can be used to allow comparison of the same structure orpart of the structure in different acquisitions or reconstructions. Insome embodiments, an algorithm can be developed, validated, and appliedin order to identify the one or more anatomical landmarks. In someembodiments, the system can be configured to utilize one or more imageprocessing, artificial intelligence (AI), and/or machine learning (ML)algorithms to automatically and/or dynamically identify the one or moreanatomical landmarks associated with the one or more arteries, vessels,and/or portions thereof within each phase or series of the image data.In some embodiments, the anatomical landmarks can comprise beginningpoints and/or endpoints associated with the one or more arteries,vessels, and/or portions thereof. In some embodiments, the anatomicallandmarks can comprise branches associated with the one or morearteries, vessels, and/or portions thereof. In some embodiments, otheranatomical landmarks can be used.

At block 2908, the system can be configured to, for one or more (or all)of the coronary arteries, branches, or segments, rank image quality foreach of the phases or series. For example, in some embodiments, analgorithm is developed, validated, and applied that is configured to,for one or more of the coronary arteries, branches, or segments, rankimage quality for across the phases or series. In some embodiments, thesystem can be configured to utilize one or more image processing,artificial intelligence (AI), and/or machine learning (ML) algorithms toautomatically and/or dynamically to determine an image quality rank.Determining an image quality rank can be based on one or more of anumber of factors including, for example, clarity and/or sharpness of arepresentation of the coronary arteries, branches, or segments within aphase or series of the image data.

At block 2940, the phase or series which shows a coronary artery,branch, or segment with the highest image quality can be identified.Notably, different coronary arteries, branches, or segments may be shownwith the highest image quality in different phases or series. Blocks2908 and 2940 can be repeated for each of the coronary arteries,branches, or segments or for as many of them are desired.Adventitiously, this allows for the identification of which phase orseries of the image data provides the best (e.g., clearest or sharpest)image of each of the identified coronary arteries.

After the phase or series representing the best image quality for eachcoronary artery is identified, at block 2911, that phase or series canbe analyzed to determine CAD measurements and/or vascular morphology forthe associated coronary artery. For example at block 2708, the systemcan be configured to perform quantitative phenotyping of atherosclerosisfor the articular coronary artery using the phase or series that hasbeen identified to correspond to the highest image quality. For example,in some embodiments, the quantitative phenotyping can be ofatherosclerosis burden, volume, type, composition, and/or rate ofprogression for the individual or patient. In some embodiments, thesystem can be configured to utilize one or more image processing,artificial intelligence (AI), and/or machine learning (ML) algorithms toautomatically and/or dynamically perform quantitative phenotyping ofatherosclerosis.

In some embodiments, as part of quantitative phenotyping, the system canbe configured to identify and/or characterize different types and/orregions of plaque, for example based on density, absolute density,material density, relative density, and/or radiodensity. For example, insome embodiments, the system can be configured to characterize a regionof plaque into one or more sub-types of plaque. For example, in someembodiments, the system can be configured to characterize a region ofplaque as one or more of low density non-calcified plaque, non-calcifiedplaque, or calcified plaque. In some embodiments, calcified plaque cancorrespond to plaque having a highest density range, low densitynon-calcified plaque can correspond to plaque having a lowest densityrange, and non-calcified plaque can correspond to plaque having adensity range between calcified plaque and low density non-calcifiedplaque. For example, in some embodiments, the system can be configuredto characterize a particular region of plaque as low densitynon-calcified plaque when the radiodensity of an image pixel or voxelcorresponding to that region of plaque is between about -189 and about30 Hounsfield units (HU). In some embodiments, the system can beconfigured to characterize a particular region of plaque asnon-calcified plaque when the radiodensity of an image pixel or voxelcorresponding to that region of plaque is between about 31 and about 350HU. In some embodiments, the system can be configured to characterize aparticular region of plaque as calcified plaque when the radiodensity ofan image pixel or voxel corresponding to that region of plaque isbetween about 351 and about 2500 HU.

In some embodiments, the lower and/or upper Hounsfield unit boundarythreshold for determining whether a plaque corresponds to one or more oflow density non-calcified plaque, non-calcified plaque, and/or calcifiedplaque can be about -1000 HU, about -900 HU, about -800 HU, about -700HU, about -600 HU, about -500 HU, about -400 HU, about -300 HU, about-200 HU, about -190 HU, about -180 HU, about -170 HU, about -160 HU,about -150 HU, about -140 HU, about -130 HU, about -120 HU, about -110HU, about -100 HU, about -90HU, about -80 HU, about -70 HU, about -60HU, about -50 HU, about -40 HU, about -30 HU, about -20 HU, about -10HU, about 0 HU, about 10 HU, about 20 HU, about 30 HU, about 40 HU,about 50 HU, about 60 HU, about 70 HU, about 80 HU, about 90 HU, about100 HU, about 110 HU, about 120 HU, about 130 HU, about 140 HU, about150 HU, about 160 HU, about 170 HU, about 180 HU, about 190 HU, about200 HU, about 210 HU, about 2950 HU, about 230 HU, about 240 HU, about250 HU, about 260 HU, about 270 HU, about 280 HU, about 290 HU, about300 HU, about 310 HU, about 320 HU, about 330 HU, about 340 HU, about350 HU, about 360 HU, about 370 HU, about 380 HU, about 390 HU, about400 HU, about 410 HU, about 420 HU, about 430 HU, about 440 HU, about450 HU, about 460 HU, about 470 HU, about 480 HU, about 490 HU, about500 HU, about 510 HU, about 520 HU, about 530 HU, about 540 HU, about550 HU, about 560 HU, about 570 HU, about 580 HU, about 590 HU, about600 HU, about 700 HU, about 800 HU, about 900 HU, about 1000 HU, about1100 HU, about 1200 HU, about 1300 HU, about 1400 HU, about 1500 HU,about 1600 HU, about 1700 HU, about 1800 HU, about 1900 HU, about 2000HU, about 2100 HU, about 29500 HU, about 2300 HU, about 2400 HU, about2500 HU, about 2600 HU, about 2700 HU, about 2800 HU, about 2900 HU,about 3000 HU, about 3100 HU, about 3200 HU, about 3300 HU, about 3400HU, about 3500 HU, and/or about 4000 HU.

In some embodiments, the system can be configured to determine and/orcharacterize the burden of atherosclerosis based at least part on volumeof plaque. In some embodiments, the system can be configured to analyzeand/or determine total volume of plaque and/or volume of low-densitynon-calcified plaque, non-calcified plaque, and/or calcified plaque. Insome embodiments, the system can be configured to perform phenotyping ofplaque by determining a ratio of one or more of the foregoing volumes ofplaque, for example within an artery, lesion, vessel, and/or the like.

In some embodiments, the system can be configured to analyze theprogression of plaque. For example, in some embodiments, the system canbe configured to analyze the progression of one or more particularregions of plaque and/or overall progression and/or lesion and/orartery-specific progression of plaque. In some embodiments, in order toanalyze the progression of plaque, the system can be configured toanalyze one or more serial images of the subject for phenotypingatherosclerosis. In some embodiments, tracking the progression of plaquecan comprise analyzing changes and/or lack thereof in total plaquevolume and/or volume of low-density non-calcified plaque, non-calcifiedplaque, and/or calcified plaque. In some embodiments, tracking theprogression of plaque can comprise analyzing changes and/or lack thereofin density of a particular region of plaque and/or globally.

Additionally or alternatively, in some embodiments, at block 2912, thecoronary arteries, branches, or segments can be visualized according tothe images identified at block 2940-e.g., those that show the coronaryarteries, branches, or segments with the highest image quality. In someembodiments, the coronary arteries, branches, or segments can bevisualized together in a “mix-and-match” approach (e.g., combiningimages from different phases or series). Visualization can be performedaccording to various methods, including volume-rendered techniques,multiplanar reformation or reconstructions (MPRs), tabular forms, orothers. In some embodiments, the visualization can use the landmarksidentified at block 2906 to align and generate a composite image. Insome embodiments, the visualization can be stored in the medical imagedatabase 2914.

In the example provided above, this approach has been described withinthe context of CT imaging of the coronary arteries. However, themethods, systems, and devices described herein can also be used withother imaging modalities and other anatomical structures as well. Forexample, the methods, systems, and devices described herein can also beused with ultrasound imaging (for example, of other arterial beds (e.g.,carotid, aorta, lower extremity, etc.)), MRI, or nuclear testing, amongothers. Thus, the methods, systems, and devices described herein canalso be applied to image reconstructions in other forms (e.g.,reconstruction of an acquired CT volume with different thickness,different kernel, or in acquisitions with EKG synchronization, such as,different timing after the R wave of the EKG). The methods, systems, anddevices described herein can also be applied to merge imaginginformation from different types of image acquisitions (single energy CTvs. spectral CT) so as to be able to reconstruct a specific structurewith a mix and or aggregation of different information (fusion) obtainin all those different components (including change through time).

In some embodiments, the methods, systems, and devices described hereincan also be applied to depict “multi-phase” or “multi-series”information in a virtual 4D way.

The methods, systems, and devices described herein also be applied toenhance the phenotypic richness of the artery/branch/segment (or other,such as structure/organ/patient) by combining methods for imagevisualization from multiple imaging modalities (e.g., CT foratherosclerosis, PET for inflammation, or other).

The methods, systems, and devices described herein can be used to fuseinformation from previous images to illustrate the change over timeafter such interventions as medications, exercise or other.

The methods, systems, and devices described herein can be used topredict the future response, such as from pharmacologic treatment oraging.

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 29B. The example computer system 2932 is incommunication with one or more computing systems 2950 and/or one or moredata sources 2952 via one or more networks 2948. While FIG. 2illustrates an embodiment of a computing system 2932, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 2932 can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 2932 can comprise an improved CAD measurement module2944 that carries out the functions, methods, acts, and/or processesdescribed herein. The improved CAD measurement and/or visualizationmodule 2944 is executed on the computer system 2932 by a centralprocessing unit 2936 discussed further below.

In general the word “module,” as used herein, refers to logic embodiedin hardware or firmware or to a collection of software instructions,having entry and exit points. Modules are written in a program language,such as JAVA, C, or C++, or the like. Software modules can be compiledor linked into an executable program, installed in a dynamic linklibrary, or can be written in an interpreted language such as BASIC,PERL, LAU, PHP, or Python and any such languages. Software modules canbe called from other modules or from themselves, and/or can be invokedin response to detected events or interruptions. Modules implemented inhardware include connected logic units such as gates and flip-flops,and/or can include programmable units, such as programmable gate arraysor processors.

Generally, the modules described herein refer to logical modules thatcan be combined with other modules or divided into sub-modules despitetheir physical organization or storage. The modules are executed by oneor more computing systems, and can be stored on or within any suitablecomputer readable medium, or implemented in-whole or in-part withinspecial designed hardware or firmware. Not all calculations, analysis,and/or optimization require the use of computer systems, though any ofthe above-described methods, calculations, processes, or analyses can befacilitated through the use of computers. Further, in some embodiments,process blocks described herein can be altered, rearranged, combined,and/or omitted.

The computer system 2932 includes one or more processing units (CPU)206, which can comprise a microprocessor. The computer system 2932further includes a physical memory 210, such as random access memory(RAM) for temporary storage of information, a read only memory (ROM) forpermanent storage of information, and a mass storage device 2934, suchas a backing store, hard drive, rotating magnetic disks, solid statedisks (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory,diskette, or optical media storage device. Alternatively, the massstorage device can be implemented in an array of servers. Typically, thecomponents of the computer system 2932 are connected to the computerusing a standards-based bus system. The bus system can be implementedusing various protocols, such as Peripheral Component Interconnect(PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) andExtended ISA (EISA) architectures.

The computer system 2932 includes one or more input/output (I/O) devicesand interfaces 2942, such as a keyboard, mouse, touch pad, and printer.The I/O devices and interfaces 2942 can include one or more displaydevices, such as a monitor, that allows the visual presentation of datato a user. More particularly, a display device provides for thepresentation of GUIs as application software data, and multi-mediapresentations, for example. The I/O devices and interfaces 2942 can alsoprovide a communications interface to various external devices. Thecomputer system 2932 can comprise one or more multi-media devices 2938,such as speakers, video cards, graphics accelerators, and microphones,for example.

The computer system 2932 can run on a variety of computing devices, suchas a server, a Windows server, a Structure Query Language server, a UnixServer, a personal computer, a laptop computer, and so forth. In otherembodiments, the computer system 2932 can run on a cluster computersystem, a mainframe computer system and/or other computing systemsuitable for controlling and/or communicating with large databases,performing high volume transaction processing, and generating reportsfrom large databases. The computing system 2932 is generally controlledand coordinated by an operating system software, such as z/OS, Windows,Linux, UNIX, BSD, PHP, SunOS, Solaris, MacOS, ICloud services or othercompatible operating systems, including proprietary operating systems.Operating systems control and schedule computer processes for execution,perform memory management, provide file system, networking, and I/Oservices, and provide a user interface, such as a graphical userinterface (GUI), among other things.

The computer system 2932 illustrated in FIG. 29B is coupled to a network2948, such as a LAN, WAN, or the Internet via a communication link 2946(wired, wireless, or a combination thereof). Network 2948 communicateswith various computing devices and/or other electronic devices. Network2948 is communicating with one or more computing systems 2950 and one ormore data sources 2952. The improved CAD measurement and/orvisualization module 2944 can access or can be accessed by computingsystems 2950 and/or data sources 2952 through a web-enabled user accesspoint. Connections can be a direct physical connection, a virtualconnection, and other connection type. The web-enabled user access pointcan comprise a browser module that uses text, graphics, audio, video,and other media to present data and to allow interaction with data viathe network 2948.

The output module can be implemented as a combination of an all-pointsaddressable display such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. The output module can be implemented to communicate with inputdevices 2942 and they also include software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements, such as menus, windows, dialogue boxes, tool bars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the output module can communicate with a set ofinput and output devices to receive signals from the user.

The computing system 2932 can include one or more internal and/orexternal data sources (for example, data sources 2952). In someembodiments, one or more of the data repositories and the data sourcesdescribed above can be implemented using a relational database, such asDB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well asother types of databases such as a flat-file database, an entityrelationship database, and object-oriented database, and/or arecord-based database.

The computer system 2932 can also access one or more databases 2952. Thedatabases 2952 can be stored in a database or data repository. Thecomputer system 2932 can access the one or more databases 2952 through anetwork 2948 or can directly access the database or data repositorythrough I/O devices and interfaces 2942. The data repository storing theone or more databases 2952 can reside within the computer system 2932.

Examples of Embodiments Relating to Improving Accuracy of CADMeasurements

The following are non-limiting examples of certain embodiments ofsystems and methods for improving accuracy of CAD measurements and/orother related features. Other embodiments may include one or more otherfeatures, or different features, that are discussed herein.

Embodiment 1: A computer-implemented method for improving accuracy ofcoronary artery disease measurements in non-invasive imaging analysis,the method comprising: accessing, by a computer system, image data of aheart of a patient, wherein the image data comprises multiple phases orseries acquired during a cardiac cycle; identifying, by the computersystem and based on the image data, one or more coronary arteries,branches, or segments associated with the heart; determining, by thecomputer system, an image quality rank for each of the one or morecoronary arteries, branches, or segments for each of the phases orseries of the image data; determining, by the computer system, whichphase or series of the image data provides the highest image qualityrank for each of the one or more coronary arteries, branches, orsegments; and determining, by the computer system, for each of the onemore coronary arteries, branches, or segments, one or more CADmeasurements or vascular morphology based on the phase or series of theimage data that provides the highest image quality rank, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereindetermining the one or more CAD measurements or vascular morphologybased on the phase or series of the image data that provides the highestimage quality rank comprises analyzing, by the computer system, thephase or series to perform quantitative phenotyping of atherosclerosis.

Embodiment 3: The computer-implemented method of Embodiment 2, thequantitative phenotyping of atherosclerosis comprises analysis of one ormore of plaque volume, plaque composition, or plaque progression.

Embodiment 4: The computer-implemented method of Embodiment 3, whereinthe quantitative phenotyping of atherosclerosis is performed based atleast in part on analysis of density values of one or more pixels of themedical image data corresponding to plaque.

Embodiment 5: The computer-implemented method of Embodiment 4, whereinthe plaque volume comprises one or more of total plaque volume,calcified plaque volume, non-calcified plaque volume, or low-densitynon-calcified plaque volume.

Embodiment 6: The computer-implemented method of Embodiment 4, whereinthe density values comprise radiodensity values.

Embodiment 7: The computer-implemented method of Embodiment 4, whereinthe plaque composition comprises composition of one or more of calcifiedplaque, non-calcified plaque, or low-density non-calcified plaque.

Embodiment 8: The computer-implemented method of Embodiment 7, whereinone or more of the calcified plaque, non-calcified plaque, oflow-density non-calcified plaque is identified based at least in part onradiodensity values of one or more pixels of the medical image datacorresponding to plaque.

Embodiment 9: The computer-implemented method of any of Embodiments 1 to8, further comprising visualizing, by the computer system, the coronaryarteries, branches, and segments based on the identified phases orseries.

Embodiment 10: The computer-implemented method of Embodiment 9, whereinvisualizing the coronary arteries, branches, and segments comprisesgenerating, by the computing system, a composite image from the phasesor series having the highest image quality rank.

Embodiment 11: The computer-implemented method of any of Embodiment 1 to10, further comprising identifying, by the computer system, one or morelandmarks within each phase or series.

Embodiment 12: The computer-implemented method of Embodiment 11, whereinthe landmarks comprise anatomical landmarks associated with the coronaryarteries, branches, and segments.

Embodiment 13: The computer-implemented method of any of Embodiments 1to 12, wherein the medical image data is obtained using an imagingtechnique comprising one or more of computed tomography (CT), x-ray,ultrasound, echocardiography, intravascular ultrasound (IVUS), MRimaging, optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), or near-field infrared spectroscopy (NIRS).

Embodiment 14: The computer-implemented method of any of Embodiments 1to 13, wherein visualizing the coronary arteries, branches, and segmentsbased on the selected phases or series comprises presenting an image ofeach coronary arteries, branches, and segments based on the selectedimages corresponding to the phase or series associated with the highestimage quality for that coronary artery, branch, or segment.

Embodiment 15: A system for improving accuracy of coronary arterydisease measurements in non-invasive imaging analysis, the systemcomprising: one or more computer readable storage devices configured tostore a plurality of computer executable instructions; and one or morehardware computer processors in communication with the one or morecomputer readable storage devices and configured to execute theplurality of computer executable instructions in order to cause thesystem to: access image data of a heart of a patient, wherein the imagedata comprises multiple phases or series acquired during a cardiaccycle; identify based on the image data, one or more coronary arteries,branches, or segments associated with the heart; determine an imagequality rank for each of the one or more coronary arteries, branches, orsegments for each of the phases or series of the image data; determinewhich phase or series of the image data provides the highest imagequality rank for each of the one or more coronary arteries, branches, orsegments; and determine for each of the one more coronary arteries,branches, or segments, one or more CAD measurements or vascularmorphology based on the phase or series of the image data that providesthe highest image quality rank,

Embodiment 16: The system of Embodiment 15, wherein determining the oneor more CAD measurements or vascular morphology based on the phase orseries of the image data that provides the highest image quality rankcomprises analyzing, by the computer system, the phase or series toperform quantitative phenotyping of atherosclerosis.

Embodiment 17: The system of Embodiment 16, wherein the quantitativephenotyping of atherosclerosis comprises analysis of one or more ofplaque volume, plaque composition, or plaque progression.

Embodiment 18: The system of Embodiment 17, wherein the quantitativephenotyping of atherosclerosis is performed based at least in part onanalysis of density values of one or more pixels of the medical imagedata corresponding to plaque.

Embodiment 19: The system of Embodiment 18, wherein the plaque volumecomprises one or more of total plaque volume, calcified plaque volume,non-calcified plaque volume, or low-density non-calcified plaque volume.

Embodiment 20: The system of Embodiment 18, wherein the density valuescomprise radiodensity values.

Embodiment 21: The system of Embodiment 18, wherein the plaquecomposition comprises composition of one or more of calcified plaque,non-calcified plaque, or low-density non-calcified plaque.

Embodiment 22: The system of Embodiment 21, wherein one or more of thecalcified plaque, non-calcified plaque, of low-density non-calcifiedplaque is identified based at least in part on radiodensity values ofone or more pixels of the medical image corresponding to plaque.

Embodiment 23: The system of any of Embodiments 15 to 22, furthercomprising visualizing, by the computer system, the coronary arteries,branches, and segments based on the identified images.

Embodiment 24: The system of Embodiment 23, wherein visualizing thecoronary arteries, branches, and segments comprises generating, by thecomputing system, a composite image from the phases or series having thehighest image quality rank.

Embodiment 25: The system of any of Embodiments 15 to 24, furthercomprising identifying, by the computer system, one or more landmarkswithin each phase or series.

Embodiment 26: The system of Embodiment 25, wherein the landmarkscomprise anatomical landmarks associated with the coronary arteries,branches, and segments.

Embodiment 27: The system of any of Embodiments 15 to 26, wherein themedical image is obtained using an imaging technique comprising one ormore of computed tomography (CT), x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), MR imaging, optical coherencetomography (OCT), nuclear medicine imaging, positron-emission tomography(PET), single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).

Embodiment 28: The system of any of Embodiments 15 to 27, whereinvisualizing the coronary arteries, branches, and segments based on theselected images comprises presenting an image of each coronary arteries,branches, and segments based on the selected images corresponding to thephase or series associated with the highest image quality for thatcoronary artery, branch, or segment.

Longitudinal Diagnosis, Risk Assessment, Characterization of HeartDisease

Various embodiments described herein relate to systems, devices, andmethods for longitudinal image-based phenotyping to enhance drugdiscovery or development. For example, some embodiments relate toimage-based phenotyping of high-risk atherosclerosis features toaccelerate drug discovery or development for coronary artery disease(CAD) or the like.

Historically, the process for developing new drugs has been a lengthyprocess involving much trial and error. In order to develop a new drug,one must first identify a target for the drug, the target being, forexample, a cellular or molecular target for the drug to act upon inorder to achieve a desired outcome in preventing and/or treating adisease. Example, targets for drugs for treating CAD include, LDLreceptors, PCSK9, Factor VII, among others. Each of these cellular orbiological targets plays, for example, a role in the process of clottingblood. By affecting one or more of these targets, the associated step inthe clotting process may be affected as a way of treating CAD.

Identifying a drug target using current methods is often imprecise andrequires considerable time (e.g., years or decades) for several reasons.Currently, several methodologies exist for identifying a target for adrug. Historically, in the drug development process, researches havegone after risk factors associated with the disease they are attemptingto treat. In the case of CAD, researchers have considered the mechanismsassociated with high cholesterol, high blood pressure, and/or highglucose. Each of these has been statistically correlated with anincreased risk of CAD, and accordingly, by endeavoring to affect themechanisms associated with these risk factors, one can hope to identifya target for treating and/or preventing surrogate by considering themechanisms of CAD risk factors as a surrogate. A problem with usingsurrogates in identifying drug delivery is that the specific mechanismsassociated with the disease are not identified. That is, there is noguarantee that the surrogate factor is associated with a cause of thedisease, and not merely a correlated effect.

Another way that targets have been sought, is by considering patientoutcomes over considerable lengths of time (e.g., 3 years, 5 years, 10years, 20 years, or longer). For example, studies can be performed thatfollow large groups of patients (e.g., 10,000 people) over long timeperiods (e.g., 10 years). Members of the patient population thatexperience CAD events can be identified, and biological markers (e.g.,collected through blood samples or other assays) of these patients canbe compared with similar biological markers in members of the patientpopulation that have not experienced CAD events. Differences between thebiological markers of the patients who experience adverse events andthose who do not can be useful in identifying targets for drugdevelopment. However, this process is lengthy as patient populationsmust be studied over significant lengths of time. Additionally, evenwith specifically identifying those patients that experience thedisease, it can be difficult to identify targets associated with thecause of the disease.

An improved method for identifying a target for drug development caninclude examining the blood or other biological specimens of those whocurrently experience the disease. This can be done in a variety of ways.For example, biological samples of those with the disease (cases) can becompared with those that do not have the disease (controls). Anotherexample, can be examining those patients on the extremes. For example,one can examine biological samples from patients who, for variousreasons, one would expect to suffer from the disease, but who do not.Similarly, it may be extremely valuable to examine biological samplesfrom people who have the disease, but do not have any risk factorscommonly associated with the disease.

In order to gain valuable insight by studying biological samples fromthose who do or do not have the disease, it is important to be able toaccurately understand and characterize the level of disease in thosepatients. Accordingly, this application contemplates leveraging theimage-based CAD measurement and analysis tools described herein toestablish baseline and/or follow-up imaging that can be used tocharacterize and quantify a patient’s disease. This imaging can becoupled with bioassay analysis to determine relationships between thebioassay analysis and the disease as a way to identify targets (e.g.,molecular or cellular targets) for drug discovery and development. Thiscan be done in several ways.

In one example, image-based CAD phenotyping can be used to identify andquantify the CAD of various patients. The same patients can undergobioassay analysis. The results of the image-based CAD phenotyping andbioassay analysis can be related for each patient. The results can becompared between patients with high levels of CAD (cases) and patientswith low levels of CAD (controls). Examining the differences in thebioassays between the case and control groups can be useful inidentifying targets for drug discovery and development.

In another example, patients can undergo image-based CAD phenotyping andassociated bioassay analysis at different points in time. For example,first image-based CAD phenotyping and associated bioassay analysis at afirst time may establish a baseline for a patient. At a later time, forexample, 1 year, 5 years, or 10 years later, or at a time when thepatient’s CAD has developed or progressed, additional image-based CADphenotyping and associated bioassay analysis can be performed.Comparison of the changes in CAD and the changes in the bioassayanalysis between the two time periods can be used to identify targetsfor drug discovery and development. This type of dynamic evaluation canbe accomplished in several ways.

In one example, upon determining that a patient’s CAD has progressed (orimproved) between the two time points, the bioassay from the initial,first time point can be analyzed to determine targets for drug discoveryor development.

In another example, upon determining that a patient’s CAD has progressed(or improved) between the two time points, the bioassay from theinitial, first time point and the bioassay from the later time point canbe examined to determine targets for drug discovery or development. Onecan examine the association between bioassay at the initial timepoint tobaseline burden or changes in disease over time. Alternatively oradditionally, one can look at the bioassay from the later time pointand, upon identification of an individual who rapidly progresses,regresses, transforms, one can look at the bioassay after the change hasoccurred. Or, one can examine the changes between the initial timepointand the later timepoint (as a parallel marker of change), for example,to examine the changes in disease in relationship to the changes inbioassay.

As described herein and shown, for example, in FIG. 30A, in someembodiments, a potential drug target for treatment of coronaryatherosclerosis is identified, and administered to an individual (block3052). At the same time, a control individual who is not administeredthe potential drug candidate is also identified (block 3052). At block3054, both the test case and the control individual can undergocontrast-enhanced CT imaging of the heart and heart arteries. At block106, a computer system can be configured to extract atherosclerosisfeatures and vascular morphology characteristics for each individual(the test case and the control).

Additionally, at block 3058, biological specimens are obtained from thetest case and control individuals. Such biological specimens caninclude, for example, saliva, blood, stool and others. Assays can beperformed to determine the relationship of coronary atherosclerosis andvascular morphology parameters to biological specimens, including forgenetics, proteomics, transcriptomics, metabolomics, microbiomics, andothers.

At block 3060, a computer system can be configured to associate theatherosclerosis features and vascular morphology characteristics by CTto the output of the biological specimen assays (e.g., specificproteomic signatures). These atherosclerosis features and vascularmorphology features can be specific and associated withclinically-manifest adverse events (e.g., MACE, MI, or death), anddisease features include volume, composition, remodeling, location,diffuseness, and direction.

In some embodiments, at block 3062, based upon the output of thebiological specimen assays associated with the second algorithm (e.g.,coronary atherosclerosis burden, high-risk plaque), biological specimenassay outputs are identified as “targets” for drug discovery ordevelopment.

In some embodiments, the principles described above can further beextended to image-based phenotyping of high-risk atherosclerosisprogression to accelerate drug discovery or development. For example,the principles can be extended by performing serial CT imaging forchanges in atherosclerosis and vascular morphology. An example methodcan include, for example, repeat CT scans performed in the future (e.g.,1 month, 1 year, 2 years). The atherosclerosis features and vascularmorphology characteristics are quantified by the aforementionedalgorithms. Afterwards, a computer system can be configured to relatethe change in atherosclerosis features and vascular morphologycharacteristics to the biological specimen assays.

In some embodiments, the computer system can further be developed toquantify the quantified changes to the biological specimen assay output(e.g., specific proteomic signatures). Based upon the output of thebiological specimen assays (e.g., proteomic signatures) that are commonto both the second and the fourth algorithms (e.g., coronary plaqueprogression, non-reduction in high-risk plaque), biological specimenassay outputs are identified as “targets” for drug discovery ordevelopment.

In some embodiments, these principles can still be extended even furtherto image-based phenotyping of atherosclerosis stabilization orprogression to identify optimal drug responders or non-responders. Forexample, the principles can be extended by performing serial CT imagingfor changes in atherosclerosis and vascular morphology. An examplemethod can include, for individuals treated with a specific drug,repeating CT scans in the future (e.g., 1 month, 1 year, 2 years). Theatherosclerosis features and vascular morphology characteristics arequantified as described above. The computer system can further beconfigured to relate the change in atherosclerosis features and vascularmorphology characteristics to the biological specimen assays.Individuals treated with this specific drug are classified as responders(e.g., reduced plaque progression) versus non-responders (e.g.,continued plaque progression, continued high-risk plaque features, newhigh-risk plaques, etc.). The computer system can further relateresponders versus non-responders to the biological specimen assayoutputs.

The approach described herein can be used with: multivariable adjustmentof CAD risk factors and treatment and patient demographics/biometrics;protein, serum or urine markers, cytologic or histologic information,diet, exercise, digital wearables; combination targets of genomics andproteomics and/or microbiomics and metabolomics, etc.; combiningdifferent image features, and/or different information from differentimage modalities (e.g., liver steatosis from an ultrasound, delayedenhancement from an MRI).

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 2 . The example computer system 3002 is incommunication with one or more computing systems 3020 and/or one or moredata sources 3022 via one or more networks 3018. While FIG. 2illustrates an embodiment of a computing system 3002, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 3002 can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 3002 can comprise an image-based phenotyping module3014 that carries out the functions, methods, acts, and/or processesdescribed herein. The image-based phenotyping module 3014 is executed onthe computer system 3002 by a central processing unit 3006 discussedfurther below.

In general the word “module,” as used herein, refers to logic embodiedin hardware or firmware or to a collection of software instructions,having entry and exit points. Modules are written in a program language,such as JAVA, C, or C++, or the like. Software modules can be compiledor linked into an executable program, installed in a dynamic linklibrary, or can be written in an interpreted language such as BASIC,PERL, LAU, PHP, or Python and any such languages. Software modules canbe called from other modules or from themselves, and/or can be invokedin response to detected events or interruptions. Modules implemented inhardware include connected logic units such as gates and flip-flops,and/or can include programmable units, such as programmable gate arraysor processors.

Generally, the modules described herein refer to logical modules thatcan be combined with other modules or divided into sub-modules despitetheir physical organization or storage. The modules are executed by oneor more computing systems, and can be stored on or within any suitablecomputer readable medium, or implemented in-whole or in-part withinspecial designed hardware or firmware. Not all calculations, analysis,and/or optimization require the use of computer systems, though any ofthe above-described methods, calculations, processes, or analyses can befacilitated through the use of computers. Further, in some embodiments,process blocks described herein can be altered, rearranged, combined,and/or omitted.

The computer system 3002 includes one or more processing units (CPU)3006, which can comprise a microprocessor. The computer system 3002further includes a physical memory 3010, such as random access memory(RAM) for temporary storage of information, a read only memory (ROM) forpermanent storage of information, and a mass storage device 3004, suchas a backing store, hard drive, rotating magnetic disks, solid statedisks (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory,diskette, or optical media storage device. Alternatively, the massstorage device can be implemented in an array of servers. Typically, thecomponents of the computer system 3002 are connected to the computerusing a standards based bus system. The bus system can be implementedusing various protocols, such as Peripheral Component Interconnect(PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) andExtended ISA (EISA) architectures.

The computer system 3002 includes one or more input/output (I/O) devicesand interfaces 3012, such as a keyboard, mouse, touch pad, and printer.The I/O devices and interfaces 3012 can include one or more displaydevices, such as a monitor, that allows the visual presentation of datato a user. More particularly, a display device provides for thepresentation of GUIs as application software data, and multi-mediapresentations, for example. The I/O devices and interfaces 3012 can alsoprovide a communications interface to various external devices. Thecomputer system 3002 can comprise one or more multi-media devices 3008,such as speakers, video cards, graphics accelerators, and microphones,for example.

The computer system 3002 can run on a variety of computing devices, suchas a server, a Windows server, a Structure Query Language server, a UnixServer, a personal computer, a laptop computer, and so forth. In otherembodiments, the computer system 3002 can run on a cluster computersystem, a mainframe computer system and/or other computing systemsuitable for controlling and/or communicating with large databases,performing high volume transaction processing, and generating reportsfrom large databases. The computing system 3002 is generally controlledand coordinated by an operating system software, such as z/OS, Windows,Linux, UNIX, BSD, PHP, SunOS, Solaris, MacOS, ICloud services or othercompatible operating systems, including proprietary operating systems.Operating systems control and schedule computer processes for execution,perform memory management, provide file system, networking, and I/Oservices, and provide a user interface, such as a graphical userinterface (GUI), among other things.

The computer system 3002 illustrated in FIG. 30B is coupled to a network3018, such as a LAN, WAN, or the Internet via a communication link 3016(wired, wireless, or a combination thereof). Network 3018 communicateswith various computing devices and/or other electronic devices. Network3018 is communicating with one or more computing systems 3020 and one ormore data sources 3022. The image-based phenotyping module 3014 canaccess or can be accessed by computing systems 3020 and/or data sources3022 through a web-enabled user access point. Connections can be adirect physical connection, a virtual connection, and other connectiontype. The web-enabled user access point can comprise a browser modulethat uses text, graphics, audio, video, and other media to present dataand to allow interaction with data via the network 3018.

The output module can be implemented as a combination of an all-pointsaddressable display such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. The output module can be implemented to communicate with inputdevices 3012 and they also include software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements, such as menus, windows, dialogue boxes, tool bars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the output module can communicate with a set ofinput and output devices to receive signals from the user.

The computing system 3002 can include one or more internal and/orexternal data sources (for example, data sources 3022). In someembodiments, one or more of the data repositories and the data sourcesdescribed above can be implemented using a relational database, such asDB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well asother types of databases such as a flat-file database, an entityrelationship database, and object-oriented database, and/or arecord-based database.

The computer system 3002 can also access one or more databases 3022. Thedatabases 3022 can be stored in a database or data repository. Thecomputer system 3002 can access the one or more databases 3022 through anetwork 3018 or can directly access the database or data repositorythrough I/O devices and interfaces 3012. The data repository storing theone or more databases 3022 can reside within the computer system 3002.

Examples of Embodiments Relating to Longitudinal Diagnosis, RiskAssessment, Characterization of Heart Disease

The following are non-limiting examples of certain embodiments ofsystems and methods for determining longitudinal diagnosis, riskassessment, characterization of heart disease and/or other relatedfeatures. Other embodiments may include one or more other features, ordifferent features, that are discussed herein.

Embodiment 1: A computer-implemented method for image-based phenotypingto enhance drug discovery or development, the method comprising:accessing, by a computer system, a first medical image of a test casepatient; analyzing, by the computer system, the first medical image ofthe test case patient to perform quantitative phenotyping ofatherosclerosis associated with the test case patient, the quantitativephenotyping of atherosclerosis comprising analysis of one or more ofplaque volume, plaque composition, or plaque progression; accessing, bythe computer system, a second medical image of a control patient;analyzing, by the computer system, the second medical image of the testcase patient to perform quantitative phenotyping of atherosclerosisassociated with the control patient, the quantitative phenotyping ofatherosclerosis comprising analysis of one or more of plaque volume,plaque composition, or plaque progression; relating, by the computersystem, outputs of assays performed on biological specimens obtainedfrom the test case patient and the control patient to theatherosclerosis features and vascular morphology characteristicsassociated with the test case patient and the control patient,respectively; and based on the related outputs of the assays andatherosclerosis features and vascular morphology characteristics,identifying, by the computer system, biological specimen assay outputsas targets for drug discovery or development, wherein the computersystem comprises a computer processor and an electronic storage medium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereinthe targets for drug discovery and development are identified based oncomparison of the test case patient to the control patient.

Embodiment 3: The computer-implemented method of Embodiment 2, whereinthe comparison of the test case patient to the control patient is basedon comparing the quantitative phenotyping of atherosclerosis of the testcase patient and the control patient.

Embodiment 4: The computer-implemented method of Embodiment 3, whereinthe comparison of the test case patient to the control patient is basedon comparing changes of the quantitative phenotyping of atherosclerosisof the test case patient and the control patient over time.

Embodiment 5: The computer-implemented method of Embodiment 4, whereinthe changes are evaluated based on quantitative phenotyping performed atgreater than two points of time.

Embodiment 6: The computer-implemented method of any of Embodiments 1 to5, wherein the comparison of the test case patient to the controlpatient is based on comparing the outputs of assays performed onbiological specimens obtained from the test case patient and the controlpatient.

Embodiment 7: The computer-implemented method of Embodiment 6, whereinthe comparison of the test case patient to the control patient is basedon comparing changes of the outputs of assays performed on biologicalspecimens obtained from the test case patient and the control patientover time.

Embodiment 8: The computer-implemented method of Embodiment 4, whereinthe changes are evaluated based on quantitative phenotyping performed atgreater than two points of time.

Embodiment 9: The computer-implemented method of any of Embodiments 1 to8, wherein the biological specimen assay outputs as targets for drugdiscovery or development comprise one or more of genomics, proteomics,transcriptomics, metabolomics, microbiomics, and epigenetics.

Embodiment 10: The computer-implemented method of any of Embodiments 1to 9, wherein the quantitative phenotyping is further comprises ananalysis of one or more of plaque remodeling, plaque location, plaquediffuseness, and plaque direction.

Embodiment 11: The computer-implemented method of any of Embodiments 1to 10, wherein the quantitative phenotyping of atherosclerosis isperformed based at least in part on analysis of density values of one ormore pixels of the medical image corresponding to plaque.

Embodiment 12: The computer-implemented method of Embodiment 11, whereinthe plaque volume comprises one or more of total plaque volume,calcified plaque volume, non-calcified plaque volume, or low-densitynon-calcified plaque volume.

Embodiment 13: The computer-implemented method of Embodiment 11, whereinthe density values comprise radiodensity values.

Embodiment 14: The computer-implemented method of Embodiment 11, whereinthe plaque composition comprises composition of one or more of calcifiedplaque, non-calcified plaque, or low-density non-calcified plaque.

Embodiment 15: The computer-implemented method of Embodiment 14, whereinone or more of the calcified plaque, non-calcified plaque, oflow-density non-calcified plaque is identified based at least in part onradiodensity values of one or more pixels of the medical imagecorresponding to plaque.

Embodiment 16: The computer-implemented method of any of Embodiments 1to 15, wherein the biologic specimens are obtained from one or more ofthe following: saliva, blood, or stool.

Embodiment 17: The computer-implemented method of any of Embodiments 1to 16, wherein the biologic specimens are analyzed to determine one ormore of genetics, proteomics, transcriptomics, metabolomics,microbiomics.

Embodiment 18: The computer-implemented method of any of Embodiments 1to 17, wherein the medical image comprises a Computed Tomography (CT)image.

Embodiment 19: The computer-implemented method of any of Embodiments 1to 10, wherein the medical image is obtained using an imaging techniquecomprising one or more of CT, x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), MR imaging, optical coherencetomography (OCT), nuclear medicine imaging, positron-emission tomography(PET), single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).

Embodiment 20: A system for improving accuracy of coronary arterydisease measurements in non-invasive imaging analysis, the systemcomprising: one or more computer readable storage devices configured tostore a plurality of computer executable instructions; and one or morehardware computer processors in communication with the one or morecomputer readable storage devices and configured to execute theplurality of computer executable instructions in order to cause thesystem to: access a first medical image of a test case patient; analyzefirst medical image of the test case patient to perform quantitativephenotyping of atherosclerosis associated with the test case patient,the quantitative phenotyping of atherosclerosis comprising analysis ofone or more of plaque volume, plaque composition, or plaque progression;access a second medical image of a control patient; analyze the secondmedical image of the test case patient to perform quantitativephenotyping of atherosclerosis associated with the control patient, thequantitative phenotyping of atherosclerosis comprising analysis of oneor more of plaque volume, plaque composition, or plaque progression;relate outputs of assays performed on biological specimens obtained fromthe test case patient and the control patient to the atherosclerosisfeatures and vascular morphology characteristics associated with thetest case patient and the control patient, respectively; and based onthe related outputs of the assays and atherosclerosis features andvascular morphology characteristics, identify biological specimen assayoutputs as targets for drug discovery or development

Embodiment 21: The system of Embodiment 20, wherein the targets for drugdiscovery and development are identified based on comparison of the testcase patient to the control patient.

Embodiment 22: The system of Embodiment 21, wherein the comparison ofthe test case patient to the control patient is based on comparing thequantitative phenotyping of atherosclerosis of the test case patient andthe control patient.

Embodiment 23 The system of Embodiment 22, wherein the comparison of thetest case patient to the control patient is based on comparing changesof the quantitative phenotyping of atherosclerosis of the test casepatient and the control patient over time.

Embodiment 24: The system of Embodiment 23, wherein the changes areevaluated based on quantitative phenotyping performed at greater thantwo points of time.

Embodiment 25: The system of any of Embodiments 20 to 24, wherein thecomparison of the test case patient to the control patient is based oncomparing the outputs of assays performed on biological specimensobtained from the test case patient and the control patient.

Embodiment 26: The system of Embodiment 25, wherein the comparison ofthe test case patient to the control patient is based on comparingchanges of the outputs of assays performed on biological specimensobtained from the test case patient and the control patient over time.

Embodiment 27: The system of Embodiment 26, wherein the changes areevaluated based on quantitative phenotyping performed at greater thantwo points of time.

Embodiment 28: The system of any of Embodiments 20 to 27, wherein thebiological specimen assay outputs as targets for drug discovery ordevelopment comprise one or more of genomics, proteomics,transcriptomics, metabolomics, microbiomics, and epigenetics.

Embodiment 29: The system of any of Embodiments 20 to 28, wherein thequantitative phenotyping is further comprises an analysis of one or moreof plaque remodeling, plaque location, plaque diffuseness, and plaquedirection.

Embodiment 30: The system of any of Embodiments 20 to 29, wherein thequantitative phenotyping of atherosclerosis is performed based at leastin part on analysis of density values of one or more pixels of themedical image corresponding to plaque.

Embodiment 31: The system of Embodiment 29, wherein the plaque volumecomprises one or more of total plaque volume, calcified plaque volume,non-calcified plaque volume, or low-density non-calcified plaque volume.

Embodiment 32: The system of Embodiment 29, wherein the density valuescomprise radiodensity values.

Embodiment 33: The system of Embodiment 29, wherein the plaquecomposition comprises composition of one or more of calcified plaque,non-calcified plaque, or low-density non-calcified plaque.

Embodiment 34: The system of Embodiment 33, wherein one or more of thecalcified plaque, non-calcified plaque, of low-density non-calcifiedplaque is identified based at least in part on radiodensity values ofone or more pixels of the medical image corresponding to plaque.

Embodiment 35: The system of any of Embodiments 20 to 34, wherein thebiologic specimens are obtained from one or more of the following:saliva, blood, or stool.

Embodiment 36: The system of any of Embodiments 20 to 35, wherein thebiologic specimens are analyzed to determine one or more of genetics,proteomics, transcriptomics, metabolomics, microbiomics.

Embodiment 37: The system of any of Embodiments 20 to 36, wherein themedical image comprises a Computed Tomography (CT) image.

Embodiment 38: The system of any of Embodiments 20 to 37, wherein themedical image is obtained using an imaging technique comprising one ormore of CT, x-ray, ultrasound, echocardiography, intravascularultrasound (IVUS), MR imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS).

Other Embodiment(s)

Although this invention has been disclosed in the context of certainembodiments and examples, it will be understood by those skilled in theart that the invention extends beyond the specifically disclosedembodiments to other alternative embodiments and/or uses of theinvention and obvious modifications and equivalents thereof. Inaddition, while several variations of the embodiments of the inventionhave been shown and described in detail, other modifications, which arewithin the scope of this invention, will be readily apparent to those ofskill in the art based upon this disclosure. It is also contemplatedthat various combinations or sub-combinations of the specific featuresand aspects of the embodiments may be made and still fall within thescope of the invention. It should be understood that various featuresand aspects of the disclosed embodiments can be combined with, orsubstituted for, one another in order to form varying modes of theembodiments of the disclosed invention. Any methods disclosed hereinneed not be performed in the order recited. Thus, it is intended thatthe scope of the invention herein disclosed should not be limited by theparticular embodiments described above.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment. Theheadings used herein are for the convenience of the reader only and arenot meant to limit the scope of the inventions or embodiments.

Further, while the methods and devices described herein may besusceptible to various modifications and alternative forms, specificexamples thereof have been shown in the drawings and are hereindescribed in detail. It should be understood, however, that theinvention is not to be limited to the particular forms or methodsdisclosed, but, to the contrary, the invention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the various implementations described and the appendedembodiments. Further, the disclosure herein of any particular feature,aspect, method, property, characteristic, quality, attribute, element,or the like in connection with an implementation or embodiment can beused in all other implementations or embodiments set forth herein. Anymethods disclosed herein need not be performed in the order recited. Themethods disclosed herein may include certain actions taken by apractitioner; however, the methods can also include any third-partyinstruction of those actions, either expressly or by implication. Theranges disclosed herein also encompass any and all overlap, sub-ranges,and combinations thereof. Language such as “up to,” “at least,” “greaterthan,” “less than,” “between,” and the like includes the number recited.Numbers preceded by a term such as “about” or “approximately” includethe recited numbers and should be interpreted based on the circumstances(e.g., as accurate as reasonably possible under the circumstances, forexample ±5%, ±10%, ±15%, etc.). For example, “about 3.5 mm” includes“3.5 mm.” Phrases preceded by a term such as “substantially” include therecited phrase and should be interpreted based on the circumstances(e.g., as much as reasonably possible under the circumstances). Forexample, “substantially constant” includes “constant.” Unless statedotherwise, all measurements are at standard conditions includingtemperature and pressure.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: A, B, or C” is intended to cover: A, B, C,A and B, A and C, B and C, and A, B, and C. Conjunctive language such asthe phrase “at least one of X, Y and Z,” unless specifically statedotherwise, is otherwise understood with the context as used in generalto convey that an item, term, etc. may be at least one of X, Y or Z.Thus, such conjunctive language is not generally intended to imply thatcertain embodiments require at least one of X, at least one of Y, and atleast one of Z to each be present.

1. A computer-implemented method for improving accuracy of coronaryartery disease measurements in non-invasive imaging analysis, the methodcomprising: accessing, by a computer system, image data of a heart of apatient, wherein the image data comprises multiple phases or seriesacquired during a cardiac cycle; identifying, by the computer system andbased on the image data, one or more coronary arteries, branches, orsegments associated with the heart; determining, by the computer system,an image quality rank for each of the one or more coronary arteries,branches, or segments for each of the phases or series of the imagedata; determining, by the computer system, which phase or series of theimage data provides the highest image quality rank for each of the oneor more coronary arteries, branches, or segments; and determining, bythe computer system, for each of the one more coronary arteries,branches, or segments, one or more CAD measurements or vascularmorphology based on the phase or series of the image data that providesthe highest image quality rank, wherein the computer system comprises acomputer processor and an electronic storage medium.
 2. Thecomputer-implemented method of claim 1, wherein determining the one ormore CAD measurements or vascular morphology based on the phase orseries of the image data that provides the highest image quality rankcomprises analyzing, by the computer system, the phase or series toperform quantitative phenotyping of atherosclerosis.
 3. Thecomputer-implemented method of claim 2, wherein the quantitativephenotyping of atherosclerosis comprises analysis of one or more ofplaque volume, plaque composition, or plaque progression.
 4. Thecomputer-implemented method of claim 3, wherein the quantitativephenotyping of atherosclerosis is performed based at least in part onanalysis of density values of one or more pixels of the medical imagedata corresponding to plaque.
 5. The computer-implemented method ofclaim 4, wherein the plaque volume comprises one or more of total plaquevolume, calcified plaque volume, non-calcified plaque volume, orlow-density non-calcified plaque volume.
 6. The computer-implementedmethod of claim 4, wherein the density values comprise radiodensityvalues.
 7. The computer-implemented method of claim 4, wherein theplaque composition comprises composition of one or more of calcifiedplaque, non-calcified plaque, or low-density non-calcified plaque. 8.The computer-implemented claim of claim 7, wherein one or more of thecalcified plaque, non-calcified plaque, of low-density non-calcifiedplaque is identified based at least in part on radiodensity values ofone or more pixels of the medical image data corresponding to plaque. 9.The computer-implemented method of claim 1, further comprisingvisualizing, by the computer system, the coronary arteries, branches,and segments based on the identified phases or series.
 10. Thecomputer-implemented method of claim 9, wherein visualizing the coronaryarteries, branches, and segments comprises generating, by the computingsystem, a composite image from the phases or series having the highestimage quality rank.
 11. The computer-implemented method of claim 1,further comprising identifying, by the computer system, one or morelandmarks within each phase or series.
 12. The computer-implementedmethod of claim 11, wherein the landmarks comprise anatomical landmarksassociated with the coronary arteries, branches, and segments.
 13. Thecomputer-implemented method of claim 1, wherein the medical image datais obtained using an imaging technique comprising one or more ofcomputed tomography (CT), x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), MR imaging, optical coherencetomography (OCT), nuclear medicine imaging, positron-emission tomography(PET), single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).
 14. The computer-implemented method ofclaim 1, wherein visualizing the coronary arteries, branches, andsegments based on the selected phases or series comprises presenting animage of each coronary arteries, branches, and segments based on theselected images corresponding to the phase or series associated with thehighest image quality for that coronary artery, branch, or segment. 15.A system for improving accuracy of coronary artery disease measurementsin non-invasive imaging analysis, the system comprising: one or morecomputer readable storage devices configured to store a plurality ofcomputer executable instructions; and one or more hardware computerprocessors in communication with the one or more computer readablestorage devices and configured to execute the plurality of computerexecutable instructions in order to cause the system to: access imagedata of a heart of a patient, wherein the image data comprises multiplephases or series acquired during a cardiac cycle; identify based on theimage data, one or more coronary arteries, branches, or segmentsassociated with the heart; determine an image quality rank for each ofthe one or more coronary arteries, branches, or segments for each of thephases or series of the image data; determine which phase or series ofthe image data provides the highest image quality rank for each of theone or more coronary arteries, branches, or segments; and determine foreach of the one more coronary arteries, branches, or segments, one ormore CAD measurements or vascular morphology based on the phase orseries of the image data that provides the highest image quality rank.16. The system of claim 15, wherein determining the one or more CADmeasurements or vascular morphology based on the phase or series of theimage data that provides the highest image quality rank comprisesanalyzing, by the computer system, the phase or series to performquantitative phenotyping of atherosclerosis.
 17. The system of claim 16,wherein the quantitative phenotyping of atherosclerosis comprisesanalysis of one or more of plaque volume, plaque composition, or plaqueprogression.
 18. The system of claim 17, wherein the quantitativephenotyping of atherosclerosis is performed based at least in part onanalysis of density values of one or more pixels of the medical imagedata corresponding to plaque.
 19. The system of claim 18, wherein theplaque volume comprises one or more of total plaque volume, calcifiedplaque volume, non-calcified plaque volume, or low-density non-calcifiedplaque volume.
 20. The system of claim 18, wherein the density valuescomprise radiodensity values.
 21. The system of claim 18, wherein theplaque composition comprises composition of one or more of calcifiedplaque, non-calcified plaque, or low-density non-calcified plaque. 22.The system of claim 21, wherein one or more of the calcified plaque,non-calcified plaque, of low-density non-calcified plaque is identifiedbased at least in part on radiodensity values of one or more pixels ofthe medical image corresponding to plaque.
 23. The system of any claim15, further comprising visualizing, by the computer system, the coronaryarteries, branches, and segments based on the identified images.
 24. Thesystem of claim 23, wherein visualizing the coronary arteries, branches,and segments comprises generating, by the computing system, a compositeimage from the phases or series having the highest image quality rank.25. The system of claim 15, further comprising identifying, by thecomputer system, one or more landmarks within each phase or series. 26.The system of claim 25, wherein the landmarks comprise anatomicallandmarks associated with the coronary arteries, branches, and segments.27. The system of claim 15, wherein the medical image is obtained usingan imaging technique comprising one or more of computed tomography (CT),x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), MRimaging, optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), or near-field infrared spectroscopy (NIRS).
 28. Thesystem of claim 15, wherein visualizing the coronary arteries, branches,and segments based on the selected images comprises presenting an imageof each coronary arteries, branches, and segments based on the selectedimages corresponding to the phase or series associated with the highestimage quality for that coronary artery, branch, or segment.